A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. ANALYSIS ON MACHINE LEARNING BASED METHODS <s> Environmental microorganisms (EMs) such as bacteria and protozoa are found in every imaginable environments. To explore functions of EMs is an important research field for environmental assessment and treatment. However, EMs are traditionally investigated through morphological analysis using microscopes or DNA analysis, which is time and money consuming. To overcome this, we introduce an innovative method which applies content-based image analysis (CBIA) to environmental microbiology. Our method classifies EMs into different categories based on features extracted from microscopic images. Specifically, it consists of three steps: The first is image segmentation which accurately extracts the region of an EM in a microscopic image with a small amount of user interaction. The second step is feature extraction where multiple features are extracted to describe different characteristics of the EM. In particular, we develop an internal structure histogram descriptor which captures the structure of the EM using angles defined on its contour. The last step is fusion which combines classification results by different features to improve the performance. Experimental results validate the effectiveness and practicability of our environmental microbiology method aided by CBIA. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. ANALYSIS ON MACHINE LEARNING BASED METHODS <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> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. ANALYSIS ON MACHINE LEARNING BASED METHODS <s> The tremendous success of machine learning algorithms at image recognition tasks in recent years intersects with a time of dramatically increased use of electronic medical records and diagnostic imaging. This review introduces the machine learning algorithms as applied to medical image analysis, focusing on convolutional neural networks, and emphasizing clinical aspects of the field. The advantage of machine learning in an era of medical big data is that significant hierarchal relationships within the data can be discovered algorithmically without laborious hand-crafting of features. We cover key research areas and applications of medical image classification, localization, detection, segmentation, and registration. We conclude by discussing research obstacles, emerging trends, and possible future directions. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. ANALYSIS ON MACHINE LEARNING BASED METHODS <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> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. ANALYSIS ON MACHINE LEARNING BASED METHODS <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> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. ANALYSIS ON MACHINE LEARNING BASED METHODS <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> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. ANALYSIS ON MACHINE LEARNING BASED METHODS <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> BIB007
In machine learning based methods as illustrated in Table. 2, supervised machine learning (SML) methods are the most frequently used ones, especially in recent years they have emerged to be very prominent. Among machine learning methods, neural network related methods are the most frequently used, this is because, with neural network, significant hierarchical relationships within the data can be discovered algorithmically without laborious hand crafted features BIB003 , thus it is easier to apply them for segmentation than other machine learning methods like SVM, which need prior hand crafted features. Moreover, because of the recent innovation of high speed computers which can handle bulk dataset and complex neural network algorithms (as seen from table 2, that most of the deep learning based techniques have been applied in recent years 2015-2018). Neural network based models are non linear models which are able to capture non-linear and complex underlying characteristics of images with high degree of accuracy, this gives them capability to handle most of segmentation challenges in microorganism images such as, uneven illumination, poor contrast and non uniform undesired small features on microorganim images BIB001 , BIB002 , BIB004 . Another challenge on microorganim images is scarcity of dataset. However, with augmentation technique enough dataset can be generated from few present dataset and applied to neural network models for better segmentation results, for example in BIB005 and BIB006 augmentation is applied to increase the dataset for training. Moreover, the application of transfer learning has boosted the use of neural network models in the segmentation of microorganisms, for example the application of transfer learning can be seen in BIB007 where VGG-16 model is used, in BIB006 where ResNet50 model is used and in BIB004 , BIB005 , where U-net model is used for segmentation. Therefore, many of the recent works are focusing on application of neural networks.