A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. TRADITIONAL MICROORGANISM SEGMENTATION 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> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> B. TRADITIONAL MICROORGANISM SEGMENTATION 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> BIB002
Traditionally, manual approaches are used for segmentation (identification) of microorganisms. The most common way is the application of morphological methods to identify microorganisms after observation under microscopes, which is done manually by biologists (experts), using some image processing softwares BIB001 , BIB002 . Fig. 1 illustrates manual way of segmenting microorganism from microscopic images of TB bacteria and Plasmodium. Although traditional methods are advantageous, like being real time and flexible to changes of examined samples characteristics, they are subjected to the following drawbacks: Time consuming, tedious work, biased to experts skills, inconsistent, expensive and the results may be affected by experts daily moods. These drawbacks prompt the need for alternative, suitable and reliable methods.