A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> A computational approach is presented for modeling and quantifying the structure and dynamics of the nematode C. elegans observed by time-lapse microscopy. Worm shape and conformations are expressed in a decoupled manner. Complex worm movements are expressed in terms of three primitive patterns-peristaltic progression, deformation, and translation. The model has been incorporated into algorithms for segmentation and simultaneous tracking of multiple worms in a field, some of which may be interacting in complex ways. A recursive Bayesian filter is used for tracking. Unpredictable behaviors associated with interactions are resolved by multiple-hypothesis tracking. Our algorithm can track worms of diverse sizes and conformations (coiled/uncoiled) in the presence of imaging artifacts and clutter, even when worms are overlapping with others. A two-observer performance assessment was conducted over 16 image sequences representing wild-type and uncoordinated mutants as a function of worm size, conformation, presence of clutter, and worm entanglement. Overall detected tracking failures were 1.41%, undetected tracking failures were 0.41%, and segmentation errors were 1.11% of worm length. When worms overlap, our method reduced undetected failures from 12% to 1.75%, and segmentation error from 11% to 5%. Our method provides the basis for reliable morphometric and locomotory analysis of freely behaving worm populations. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Segmentation of Zeihl-Neelsen tissue slide images is an important step in computer-assisted tuberculosis bacilli detection. In Zeihl-Neelsen tissue slide image, colour is the most prominent feature to detect the presence of tuberculosis bacilli from its image. In this paper, an automatic colour image segmentation using neural network is proposed. The segmentation is based on supervised approach using Hybrid Multilayered Perceptron network. Colour images are converted to HSI colour space before being segmented by the network. The colour image are characterised in HSI colour space for better colour representation. Experimental results indicate that the proposed method provides good segmentation on variety of Ziehl-Neelsen tissue slide images. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> The appearance of crystals, casts, red blood cells, white blood cells and bacteria or yeast in urine sediment is a major clinical significance. It provides important information for both diagnosis and prognosis. However, low contrast against the background, less illuminating environment and an existent of complicated components on the microscopic urine sediment image need more sophisticated method to analyze. In this paper, we present a conventional method to segment the urine-sediment visual component by using feedforward-backpropagation algorithm of neural network. Background color was used as a main feature in the segmentation process. Experimental result shows that our proposed method provides quite satisfactory segmentation. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Human intestinal parasites constitute a problem in most tropical countries, causing death or physical and mental disorders. Their diagnosis usually relies on the visual analysis of microscopy images, with error rates that may range from moderate to high. The problem has been addressed via computational image analysis, but only for a few species and images free of fecal impurities. In routine, fecal impurities are a real challenge for automatic image analysis. We have circumvented this problem by a method that can segment and classify, from bright field microscopy images with fecal impurities, the 15 most common species of protozoan cysts, helminth eggs, and larvae in Brazil. Our approach exploits ellipse matching and image foresting transform for image segmentation, multiple object descriptors and their optimum combination by genetic programming for object representation, and the optimum-path forest classifier for object recognition. The results indicate that our method is a promising approach toward the fully automation of the enteroparasitosis diagnosis. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Bacterial image segmentation and classification is an important problem because bacterial appearance can vary dramatically based on environmental conditions. Further, newly isolated species may exhibit phenotypes previously unseen. Conventionally, biologists identify bacteria using colony morphology, biochemical properties, or molecular phylogenetic approaches. However, these phylogenetic classification approaches do not provide predictive information about the colony morphology that is expected to result from the growth of these bacteria on agar, or how these morphological phenotypes might vary in the presence of other bacterial species. In this paper, we propose a framework to automatically identify and classify regions of bacterial colony images and correspond them across different images from different contexts. Importantly, this approach does not require prior knowledge of species' appearances. Rather, our method assumes that images contain one or more bacteria from a pool of bacteria, and learns morphological features relevant to distinguishing between bacteria in this pool. Our method first segments the image into regions covering the bacterial colonies, agar, plate, and various border artefacts. To achieve this, we use an unsupervised deep learning technique, Convolutional Deep Belief Network (CDBN). This technique provides a deep representation of small image patches. Using this high-level representation instead of raw pixel intensities, we train a support vector machine (SVM). The trained SVM accurately classifies foreground and background patches. Once the foreground patches are identified, we train a supervised deep learning method, Convolutional Neural Network (CNN), that predicts which bacterial colonies from the pool occur in a query image. Experimental results demonstrate that our method outperforms other classical methods on segmentation and classification. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Tuberculosis is a disease with one of the most leading causes of deaths in the world, however, its fatality index could be reduced if it is diagnosed and treated on time. The Ziehl-Neelsen stained sputum smear method is the most used for bacilli detection and for developing a proper diagnosis by the specialist. Nevertheless, these stained images do not always present an adequate contrast, then, the elaboration of a reliable diagnosis is a complex, time consuming and a difficult process. This research proposes an alternative method to perform automatic bacilli segmentation in Ziehl-Neelsen images using Adaptive Signal Processing techniques, like the Least Mean Squares and Reduced Rank with Eigendecomposition algorithms. The quantitative results achieved, in correlation and true positives detection, are encouraging and suggest the use of this approach as a feasible alternative, when compared with the classical segmentation techniques, for automatic bacilli segmentation in the Ziehl-Neelsen images. <s> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Environmental Microorganisms (EMs), such as Epistylis and Rotifera, are very tiny living beings in human environments and decompose pollutants as their nutrition. The classification of EMs plays a fundamental role for establishing sustainable ecosystem. However, this is traditionally done by molecular methods using DNA or RNA analysis, or morphological methods using microscopes. These methods require huge monetary costs or manual efforts. In this paper, we propose an EM classification method by directly processing microscopic images. We especially address two problems: The first is a small training dataset problem. Because EM samples are collected from unstable real-world environments, it is difficult to stably obtain a large number of EM images. To effectively represent scarce training images, we use Sparse Coding (SC) which extracts sufficient local features from an image and reconstructs it by a sparse linear combination of bases. The second is a noisy image problem. Because EM samples are often found in dirty regions, their images often show a noisy background. To overcome this, we use Weakly Supervised Learning (WSL) which jointly performs the localisation and classification of EMs by examining the local information in training images. Experimental results on real-world images show the effectiveness and potential of our system. <s> BIB007 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Bodies of freshwater act as home to many different types of organisms, including algae. These algae can cause harm when something called a harmful algal bloom takes place, and as such it is desired to classify algae in micro-image samples from the freshwater bodies before a bloom occurs. This paper presents a novel method for improving the quality of the algae micro-image and segmenting the algae in the micro-image, two of the steps involved in the automatic recognition and classification of algae in images. First, the algae image quality was improved through the use of the Retinex enhancement technique. Then, the algae in the improved quality image was segmented from the background using a support vector machine. Experimental results indicate that the detection rate of the proposed method is over 95%. <s> BIB008 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> We propose a novel deep architecture, SegNet, for semantic pixel wise image labelling. SegNet has several attractive properties; (i) it only requires forward evaluation of a fully learnt function to obtain smooth label predictions, (ii) with increasing depth, a larger context is considered for pixel labelling which improves accuracy, and (iii) it is easy to visualise the effect of feature activation(s) in the pixel label space at any depth. SegNet is composed of a stack of encoders followed by a corresponding decoder stack which feeds into a soft-max classification layer. The decoders help map low resolution feature maps at the output of the encoder stack to full input image size feature maps. This addresses an important drawback of recent deep learning approaches which have adopted networks designed for object categorization for pixel wise labelling. These methods lack a mechanism to map deep layer feature maps to input dimensions. They resort to ad hoc methods to upsample features, e.g. by replication. This results in noisy predictions and also restricts the number of pooling layers in order to avoid too much upsampling and thus reduces spatial context. SegNet overcomes these problems by learning to map encoder outputs to image pixel labels. We test the performance of SegNet on outdoor RGB scenes from CamVid, KITTI and indoor scenes from the NYU dataset. Our results show that SegNet achieves state-of-the-art performance even without use of additional cues such as depth, video frames or post-processing with CRF models. <s> BIB009 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> 2016 International Joint Conference on Neural Networks (IJCNN), 24 -29 July 2016.Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website. <s> BIB010 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Chaetoceros is one of the largest marine genus of phytoplankton and plays an important role in ocean ecosystem and global climate. Automatic segmentation of Chaetoceros is a challenging task due to the species' biomorphic characteristics. In this paper, we present a novel method using pixel-wise classification by Support Vector Machine (SVM) [1] combined with Grayscale Surface Direction Angle Model (GSDAM) [2] for the segmentation of Chaetoceros microscope images. Firstly, the pixel-level features are acquired by the five maps of GSDAM, which are used as the input of SVM classifier. Secondly, the SVM classifier is trained by the pixel samples automatically selected from connected region pre-segmentation result. Then, the trained SVM classifier is applied to classify all the image pixels into two classes (Object and Background). Finally some post-processing are done to generate the final segmentation map. Our method not only takes advantage of biomorphic characteristics information of Chaetoceros, but also the ability of SVM classification. Experimental results show that our method achieves effective segmentation performance, yielding high setae information reserving. <s> BIB011 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> We present a method for foreground segmentation of yeast cells in the presence of high-noise induced by intentional low illumination, where traditional approaches (e.g., threshold-based methods, specialized cell-segmentation methods) fail. To deal with these harsh conditions, we use a fully-convolutional semantic segmentation network based on the SegNet architecture. Our model is capable of segmenting patches extracted from yeast live-cell experiments with a mIOU score of 0.71 on unseen patches drawn from independent experiments. Further, we show that simultaneous multi-modal observations of bio-fluorescent markers can result in better segmentation performance than the DIC channel alone. <s> BIB012 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Abstract The labeling of Environmental Microorganisms (EM) which help decomposing pollutants, plays a fundamental role for establishing sustainable ecosystem. We propose an environmental microorganism classification engine that can automatically analyze microscopic images using Conditional Random Fields (CRF) and Deep Convolutional Neural Networks (DCNN). First, to effectively represent scarce training images, a DCNN pre-trained for image classification using a large amount of data is re-purposed to our feature extractor that distils pixel-level features in microscopic images. In addition, pixel-level classification results by such features can be refined using global features that describe the whole image in toto. Finally, our CRF model localizes and classifies EMs by considering the spatial relations among DCNN-based features, and their relations to global features. The experimental results have shown 94.2% of overall segmentation accuracy and up to 91.4% mean average precision of the results. <s> BIB013 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Deep learning based models have had great success in object detection, but the state of the art models have not yet been widely applied to biological image data. We apply for the first time an object detection model previously used on natural images to identify cells and recognize their stages in brightfield microscopy images of malaria-infected blood. Many micro-organisms like malaria parasites are still studied by expert manual inspection and hand counting. This type of object detection task is challenging due to factors like variations in cell shape, density, and color, and uncertainty of some cell classes. In addition, annotated data useful for training is scarce, and the class distribution is inherently highly imbalanced due to the dominance of uninfected red blood cells. We use Faster Region-based Convolutional Neural Network (Faster R-CNN), one of the top performing object detection models in recent years, pre-trained on ImageNet but fine tuned with our data, and compare it to a baseline, which is based on a traditional approach consisting of cell segmentation, extraction of several single-cell features, and classification using random forests. To conduct our initial study, we collect and label a dataset of 1300 fields of view consisting of around 100,000 individual cells. We demonstrate that Faster R-CNN outperforms our baseline and put the results in context of human performance. <s> BIB014 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset. <s> BIB015 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Leishmaniasis is considered a neglected disease that causes thousands of deaths annually in some tropical and subtropical countries. There are various techniques to diagnose leishmaniasis of which manual microscopy is considered to be the gold standard. There is a need for the development of automatic techniques that are able to detect parasites in a robust and unsupervised manner. In this paper we present a procedure for automatizing the detection process based on a deep learning approach. We train a U-net model that successfully segments leismania parasites and classifies them into promastigotes, amastigotes and adhered parasites. <s> BIB016 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> We propose a novel unsupervised method for extracting objects from urine microscopy images and also applied U-net for extracting these objects. We fused these proposed methods with a known edge thresholding technique from an existing work on segmentation of urine microscopic images. Comparison between our proposed methods and the existing work showed that for certain object types the proposed unsupervised method with or without edge thresholding outperforms the other methods, while in other cases the U-net method with or without edge thresholding outperforms the other methods. Overall the proposed unsupervised method along with edge thresholding worked the best by extracting maximum number of objects and minimum number of artifacts. On a test dataset, the artifact to object ratio for the proposed unsupervised method was 0.71, which is significantly better than that of 1.26 for the existing work. The proposed unsupervised method along with edge thresholding extracted 3208 objects as compared to 1608 by the existing work. To the best of our knowledge this is the first application of Deep Learning for extraction of clinically significant objects in urine microscopy images. <s> BIB017 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> In this paper, we introduce a new method for the segmentation of bacterial colonies in solid agar plate images. The proposed approach comprises two contributions. First, a simple but nonetheless effective engine is devised to generate synthetic plate images. This engine overlays bacterial colony patches to existing background images, taking into account both the local appearance of the background and the intrinsic opacity of the bacterial colonies. Therefore, a scalable alternative to the human ground–truth supervision—often difficult to obtain in medical imaging, due to privacy issues and scarcity of data—is provided. Then, synthetic generated data, together with few annotated images, were used to train a Fully–Convolutional Network. Such network is actually effective in separating bacterial colonies from the background. Finally, we discuss the role of the generation of synthetic images, conducting experiments that show how their inclusion improves the performances of the segmentation network, producing very encouraging results. <s> BIB018 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> In many biomedical applications, successful training of Convolutional Neural Networks (CNNs) is restricted by an insufficient amount of annotated images. Although image augmentation can help training CNNs from a relatively small image set, in many applications, the objects of interest cannot be accurately delineated due to their fuzzy shape, image quality or a limitation in time, experience or knowledge of the expert performing the annotation. We propose an approach for training a CNN for segmentation of images with minimal annotation. The annotation consists of center points or lines of target objects of approximately known size. We demonstrate this approach in the application of Rift Valley virus segmentation in a challenging transmission electron microscopy image dataset. Our method achieves a Dice score of 0.900 and intersection over union of 0.831. Using the suggested minimal annotation training is particularly useful for applications in which full object annotations are not available or feasible. <s> BIB019 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Protozoa detection and identification play important roles in many practical domains such as parasitology, scientific research, biological treatment processes, and environmental quality evaluation. Traditional laboratory methods for protozoan identification are time-consuming and require expert knowledge and expensive equipment. Another approach is using micrographs to identify the species of protozoans that can save a lot of time and reduce the cost. However, the existing methods in this approach only identify the species when the protozoan are already segmented. These methods study features of shapes and sizes. In this work, we detect and identify in the images of cysts and oocysts of various species such as: Giardia lamblia, Iodamoeba butschilii, Toxoplasma gondi, Cyclospora cayetanensis, Balantidium coli, Sarcocystis, Cystoisospora belli and Acanthamoeba, which have round shapes in common and affect seriously to human and animal health. We propose Segmentation-driven RetinaNet to automatically detect, segment, and identify protozoans in their micrographs. By applying multiple techniques such as transfer learning, and data augmentation techniques, and dividing training samples into life-cycle stages of protozoans, we successfully overcome the lack of data issue in applying deep learning for this problem. Even though there are at most 5 samples per life-cycle category in the training data, our proposed method still achieves promising results and outperforms the original RetinaNet on our protozoa dataset. <s> BIB020 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> A. SUPERVISED MACHINE LEARNING (SML) <s> Activated sludge (AS) is a biological treatment process that is employed in wastewater treatment plants. Filamentous bacteria in AS plays an important role in the settling ability of the sludge. Proper settling of the sludge is essential for normal functionality of the wastewater plants, where filamentous bulking is always a persistent problem preventing sludge from settling. The performance of AS plants is conventionally monitored by physico-chemical procedures. An alternative way of monitoring the AS in wastewater treatment process is to use image processing and analysis. Good performance of the image segmentation algorithms is important to quantify flocs and filaments in AS. In this article, an algorithm is proposed to perform segmentation of filaments in the phase contrast images using phase stretch transform. Different values of strength (S) and warp (W) are tested to obtain optimum segmentation results and decrease the halo and shade-off artefacts encountered in phase contrast microscopy. The performance of the algorithm is assessed using DICE coefficient, accuracy, false positive rate (FPR), false negative rate (FNR) and Rand index (RI). Sixty-one gold approximations of ground truth images were manually prepared to assess the segmentation results. Thirty-two of them were acquired at 10× magnification and 29 of them were acquired at 20× magnification. The proposed algorithm exhibits better segmentation performance with an average DICE coefficient equal to 52.25%, accuracy 99.74%, FNR 41.8% and FPR 0.14% and RI 99.49%, based on 61 images. <s> BIB021
In BIB001 , a fully automated segmentation system for modeling and quantification of shape and motion of nematode C. elegans is introduced, where by assuming that the statistical properties of the background and foreground pixels remain unchanged during a time-lapse experiment, Neyman-peason model, which computes a likelihood ratio to classify the foreground pixels is applied to segment worms from background, then features for determining shape and motion are extracted. In the experiments, 16 image sequences representing four mutant stains of worms are used, with each sequence consisting of 150 frames at resolution of 1380×1032 pixels. Images consisting of overlapping worms are tested separately with non overlapping worms images. Finally, the segmentation accuracy for non overlapping and overlapping images obtained are 98.77% and 94.61%, respectively. A supervised neural network is proposed in BIB002 to segment tuberculosis bacilli from ZN stained tissue. Since HSI color model has better representation capability, RGB to HIS conversion is done first, then these images are used for training and testing the network. Segmentation is done using hybrid multilayered perception (HMLP) neural network, which has been trained using modified recursive prediction error (MRPE) algorithm and uses sigmoid as activation function. 6802 images cropped at different sizes (42 × 54, 38×43, 50×56 and 800×600 pixels) are used for training and testing the network. This approach achieves impressive segmentation results with an accuracy above 97.75%, for all the tested groups. In  , a detection system to reduce false positive results in automatic detection of tuberculosis is proposed. In this system, the results of detection performed by support vector machine(referred to computer aided detection (CAD)) network are passed to stepwise binary decision classifier (SWC) for reduction of false positive (FP). In the experiments, 1803 TB positive and 6092 non TB positive images are used, and the results show the decrease in different types of false positive (FP) from CAD to SWC stages while optimizing true positive (TP). A neural network is used for segmentation of urine sediments (bacteria, yeast and blood cells) from low contrast images BIB003 , where a feed forward back propagation neural network is trained and tested using 24 bits RGB images of size 2048 × 1536 pixels containing urine sediments (bacteria, yeast, blood cells and other casts). The network is derived from the general least mean square (LMS) algorithm with only three layers. Experimental results show that, sediment counts obtained are almost similar to manual counts. In  , a segmentation algorithm for tuberculosis bacteria is applied so as to meet the WHO standard of fault diagnosis of less than 5.00%, where image contrast enhancement is done first, then RGB image intensities are found for all images, then training and evaluation of Naïve Bayes classifier is done using the intensities of RGB images as features. During performance evaluation of the proposed algorithm, 2063 RGB images taken from Ziehl-Neelsen sputum samples are used for training the classifier, then an accuracy value of 96.90% is obtained from the resultant confusion matrix. In BIB004 , an automatic segmentation and classification approach is applied to segment and classify human intestinal protozoa in feces. Due to the fact that most parasites are elliptical, the image segmentation step aims to locate candidate objects by ellipse matching and to define their spatial extent using image foresting transform technique (IFT), before IFT, image are enhanced by a Sobel gradient operator for an IFT-based object delineation. Then features are extracted and used to train and evaluate the optical path forest (OPF) classifier. In the experiments, 793 2D images containing 112 intestinal parasites and about 5142 impurities are used. Finally the segmentation accuracy of 98.22% is achieved. An approach for segmentation and classification of bacteria is presented in BIB005 . Where, image patches containing only bacteria (foreground) of different species and others containing background with respective labels are used for training and testing a convolutional deep belief network (CDBN). CDBN is trained to obtain features for the preprocessed patches. Then a patch-level SVM model is trained to predict background or foreground, for the patches represented by the features resulted from training the CDBN. CDBN uses max pooling of size 2x2 which is small to make the model less sensitive to small shifts. Finally, patches detected as foreground are used to train and test the convolutional neural network (CNN) for classification. 862 images of size 128 × 128 pixels are used. The results of CNN are compared with k-NN and SVM (k-NN and SVM use SIFT features). The CNN outperforms the two other classification approaches with an accuracy of 62.10%. In BIB006 , two approaches for segmentation of TB bacteria from ZN stained samples are introduced. Feature vectors depicting 18 attributes for each pixel of the TB bacilli in the images, are applied to least mean square (LMS) and reduced rank with eigen decomposition algorithms separately, for segmentation. In the experiments, 80 RGB ZN stained images used. One image of size 684 × 912 pixels is used for training and 79 images of size 2736 × 3648 pixels are used for testing. The results of LMS and reduced rank algorithms are compared with segmentation results of classical methods and the proposed methods outperform with recall of 93.54% and 93.56% for LMS and reduced rank, respectively. In BIB007 , in order to overcome the problem of low number of dataset and noisy images of environmental microorganisms (EM), a weakly supervised learning system is proposed for segmentation and classification. Sparse coding method is applied for segmentation and feature extraction. It first learns a set of bases from random image patches, where each base represents a characteristic patch pattern. An image is then represented by a sparse linear combination of these bases, which captures high-level features of the image, and converts raw pixel values into an effective region-based representation (This resolves the shortage of less training images). Then the classification is accomplished using region based support vector machine (RBSVM) and finally late fusion is done to optimize classification results. In the experiments, 20 images which are taken from each of 15 classes of environmental microorganisms are used (example of classes are Actinophrys, Arcella, Codosiga, Colpoda, Paramecium and Vorticella). The proposed method for feature extraction and classification (NNSC and RBSVM) is compared with, bag of visual word features (BoVW) and linear SVM, the result of the proposed combination is more robust. Under the same context in BIB013 , the DeepLab-VGG16 model is trained with EM images to generate pixel level features and global features, then these features are used to train the random forest (RF) classifier, then trained RF classifiers (for each image class) are used as the unary potentials by the conditional random field (CRF) model. Finally together with the pairwise potentials, the CRF model is applied to localize and label the objects of interest in the test environmental microscopic images. In the experiments, images from 20 EMs are used and 20 images are captured for each class. In order to evaluate the segmentation results, the proposed method for segmentation (local-global CRF) is compared with dense-CRF, denseCRF org (which uses DeepLab-VGG16 for segmentation) and full convolutional network (FCN). Finally the proposed method outperforms with an average accuracy of 94.20%. In BIB008 , a segmentation system is designed to monitor the algae in water bodies so as to get rid of harmful algal bloom, where image enhancement (sharpening) is applied first using multi-scale retinex filter, then SVM is trained on three prominent spacial features for segmentation. These features are mean and the standard deviation of all pixels on a small blocks and frequency of different areas in the image, which is calculated using wavelet transform technique. In the experiments, 200 images are used for training the classifier in which 100 images have algae only and other 100 have background only. The images include three species of algae, Anabaena, Aphanizomenon and Microcystic. The obtained results present an average detection rate (accuracy) of over 95.00%. To overcome the scarcity of dataset in segmentation of bioimages, a CNN network is introduced in BIB010 . It is based on FCN however, it has five convolutional layers using PReLU, with one max pooling layer attached to the first layer, attached to the last layer is a deconvolutional layer, which upsamples the feature maps before it is given to a convsoftmax layer. The use of trainable deconvolutional layer enables segmentation accuracy of 97.30% on few tested dataset of C.elegans. To evaluate the performance of the proposed network, 76 notarized RGB images of size 501×501 pixels, with their corresponding binary ground truth are used for training, 19 for validation and 2 images for testing. In BIB011 , an automatic segmentation system is introduced to segment Chaetoceros marine phytoplankton, where pixel wise features are extracted using gray scale surface direction angle model (GSDAM), then segmentation is done using these features on a SVM. In the experiments, RGB images containing different species of Chaetoceros are used and the results are visually evaluated as in Fig. 16 . To enable classification of different stages of plasmodium vivax infected blood cells in BIB014 , an object detection framework faster region-based convolutional neural network (Faster R-CNN) is used to detect objects red blood cell (RBC) vs non-RBC, by forming the bounding boxes. Then non-RBC objects are fed into AlexNet to classify them into seven categories (RBC, leukocyte, gametocyte, ring, trophozoite, and schizont). During experiments, 1300 RGB images of size 448 × 448 pixels are used for training, validation and testing. Comparing with human annotators, the proposed method achieves an accuracy of 98.00%. For successful segmentation of yeast cells in poor contrast and low illumination images captured from live cells in BIB012 , a SegNet based neural network is deployed. In contrast to the original SegNet BIB009 , the architecture input shape size used is 64 × 64 × 1. To improve segmentation results training images are captured at different fluorescent modalities (DIC only, DIC 488 and DIC 561nm). In the experiments, 6000 2D image patches each containing single yeast cell are used for training while 1200, 1200 image patches are used for testing and validation. The result of 71.72% mean Intersection Union is obtained. A fully convolutional network (FCN) is applied on identification of feline calicivirus in BIB015 . By varying the filter size of the first layer in 3 × 3, 5 × 5, 7 × 7, 9 × 9, and 11 × 11 size, the optimal size for better performance is found to be 7 × 7. The performance of FCN is improved monotonically with the increased filter size. However, larger filter sizes make the analyzed region narrower due to the characteristic of convolutional computation. The network is evaluated using 18 images of pixel size 128 × 128 for training and 17 for validation. The proposed FCN achieves results of 99.70% recall. U-net based model is deployed in BIB016 to segment leishmania parasite. High-class imbalance in dataset is tackled by using generalized dice loss as loss function and adoption of two stage non uniform sampling scheme to select training patches. The network is first trained during some epochs (40 epochs) using patches that contain at least 40.00% of pixels from any of the three parasite classes (promastigote, amastigote, or adhered), then uniform sampling of all patches is used for the following epochs. From 45 dataset of size 224 × 224 pixels, 37 images are used for training. Results of 82.30% recall is achieved. In BIB017 , different segmentation methods are applied on extraction of objects from low contrast urine microscopic images, applied methods are unsupervised method (Sauvola's method as a key component), U-net method, unsupervised with edge thresholding, U-net with edge thresholding. In the experiments, 329 urine sample images are used. These images contain different types of urine objects such are bacteria, crystal, cast, epithelial cell, red blood cells, white blood cells and yeast. For training the U-net model, 4150 masks (patches) of size 64 × 64 pixels are manually generated from these 329 images. In BIB018 , a pyramid scene parsing (PSP) network (built on ResNet model) is applied in bacterial colony segmentation. To enlarge the receptive field of the network a set of dilated convolutions replaces standard convolutions in the ResNet part of the network. Then, a pyramid pooling module is used to gather context information, followed by both an upsampling and a concatenation layer, to form the final feature representation. During performance evaluation, 324 images of microbial Haemolysis are used, in which 221 images are used for training and 103 for testing the network. Finally, the segmentation accuracy above 98.00% is achieved. A segmentation system for monitoring floc and filaments of activated sludge from phase contrast images is introduced in BIB021 , where RGB to gray scale conversion is applied first, followed Gaussian low filter and finally segmentation is done using phase stretch transform (PST). With PST, image is converted to frequency (phase) domain by performing 2D fast Fourier transform (FFT), then to extract edges, phase image is binarized using one-level thresholding, the output is obtained by 2D IFFT. During experiments, 61 RGB phase contrast microscopic images of size 1224 × 960 pixels, taken from samples of water treatment plants are used. The proposed algorithm exhibits segmentation performance with an accuracy of 99.74%. In BIB019 , a convolution neural network based segmentation system is used to tackle the problem of insufficient dataset, where, augmentation is performed at angles 0, 90, 180, 270 degree to increase the datasets for training a U-net model. In order to train the model with the minimal annotated data, it is modified to use Dice coefficient as the loss function. In the experiments, 95 training images of rift valley virus are augmented by rotations and mirroring which resulted in 760 images. The images are of size 508×508 pixels. Finally the proposed CNN achieves the results of 90.00% dice score. Fig. 17 shows the proposed U-Net based CNN architecture. In order to be able to segment protozoa image automatically, a region based convolutional model is proposed in BIB020 . In this approach segmentation is done using ResNet50 model and feature pyramid network (FPN) to produce segmentation masks then RetinaNet is used to identify species of protozoa. In the experiments, 38 images are used for training and 31 for testing (resolution of images vary from 824 × 941 to 4086 × 1725 pixels) and augmentation is used to increase the dataset by rotating images at intervals of 10 degree about the center of bounding boxes of protozoa images. Example of protozoa classes which are used for testing are Giardia lamblia, Iodamoeba and Butschilii. Evaluation of the proposed segmentation method is done using mAP which is the combination of precision and recall and maximum of 89.00% mAP is achieved. Fig. 18 shows the segmentation network.