A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has received less attention. In this work we hope to help bridge the gap between the success of CNNs for supervised learning and unsupervised learning. We introduce a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrate that they are a strong candidate for unsupervised learning. Training on various image datasets, we show convincing evidence that our deep convolutional adversarial pair learns a hierarchy of representations from object parts to scenes in both the generator and discriminator. Additionally, we use the learned features for novel tasks - demonstrating their applicability as general image representations. <s> BIB001 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models. We train a convolutional semantic segmentation network along with an adversarial network that discriminates segmentation maps coming either from the ground truth or from the segmentation network. The motivation for our approach is that it can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net. Our experiments show that our adversarial training approach leads to improved accuracy on the Stanford Background and PASCAL VOC 2012 datasets. <s> BIB002 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix software associated with this paper, a large number of internet users (many of them artists) have posted their own experiments with our system, further demonstrating its wide applicability and ease of adoption without the need for parameter tweaking. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either. <s> BIB003 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> Semantic segmentation is a fundamental problem in biomedical image analysis. In biomedical practice, it is often the case that only limited annotated data are available for model training. Unannotated images, on the other hand, are easier to acquire. How to utilize unannotated images for training effective segmentation models is an important issue. In this paper, we propose a new deep adversarial network (DAN) model for biomedical image segmentation, aiming to attain consistently good segmentation results on both annotated and unannotated images. Our model consists of two networks: (1) a segmentation network (SN) to conduct segmentation; (2) an evaluation network (EN) to assess segmentation quality. During training, EN is encouraged to distinguish between segmentation results of unannotated images and annotated ones (by giving them different scores), while SN is encouraged to produce segmentation results of unannotated images such that EN cannot distinguish these from the annotated ones. Through an iterative adversarial training process, because EN is constantly “criticizing” the segmentation results of unannotated images, SN can be trained to produce more and more accurate segmentation for unannotated and unseen samples. Experiments show that our proposed DAN model is effective in utilizing unannotated image data to obtain considerably better segmentation. <s> BIB004 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> Semantic segmentation constitutes an integral part of medical image analyses for which breakthroughs in the field of deep learning were of high relevance. The large number of trainable parameters of deep neural networks however renders them inherently data hungry, a characteristic that heavily challenges the medical imaging community. Though interestingly, with the de facto standard training of fully convolutional networks (FCNs) for semantic segmentation being agnostic towards the `structure' of the predicted label maps, valuable complementary information about the global quality of the segmentation lies idle. In order to tap into this potential, we propose utilizing an adversarial network which discriminates between expert and generated annotations in order to train FCNs for semantic segmentation. Because the adversary constitutes a learned parametrization of what makes a good segmentation at a global level, we hypothesize that the method holds particular advantages for segmentation tasks on complex structured, small datasets. This holds true in our experiments: We learn to segment aggressive prostate cancer utilizing MRI images of 152 patients and show that the proposed scheme is superior over the de facto standard in terms of the detection sensitivity and the dice-score for aggressive prostate cancer. The achieved relative gains are shown to be particularly pronounced in the small dataset limit. <s> BIB005 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> Hematoxylin and Eosin stained histopathology image analysis is essential for the diagnosis and study of complicated diseases such as cancer. Existing state-of-the-art approaches demand extensive amount of supervised training data from trained pathologists. In this work we synthesize in an unsupervised manner, large histopathology image datasets, suitable for supervised training tasks. We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples. Our main contribution is that the synthetic images are not only realistic, but also representative (in reference styles) and relatively challenging for training task-specific CNNs. We test our method for nucleus segmentation using images from four cancer types. When no supervised data exists for a cancer type, our method without supervision cost significantly outperforms supervised methods which perform across-cancer generalization. Even when supervised data exists for all cancer types, our approach without supervision cost performs better than supervised methods. <s> BIB006 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> The morphology of cancer cells is widely used by pathologists to grade stages of cancers. Accurate cancer cell segmentation is significant to obtain quantitative diagnosis. We proposed a dual contour-enhanced adversarial network to solve this challenge. The distance-transformed and contour-highlighted masks, and adversarial network are incorporated to improve individual cell segmentation capability. By evaluating quantitative individual cell segmentation results on 2017 MICCAI Digital Pathology Challenge, our method achieved best balance between precision and recall rate of individual cell segmentation compared to state-of-the-art cell segmentation methods. <s> BIB007 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a unique architecture, termed Rib Cage, which are trained simultaneously and together define a min-max game resulting in an accurate segmentation of a given image. Our approach has two main strengths, similar to the GAN, the method does not require a formulation of a loss function for the optimization process. This allows training on a limited amount of annotated data in a weakly supervised manner. Promising segmentation results on real fluorescent microscopy data are presented. The code is freely available at: this https URL <s> BIB008 </s> A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential <s> D. OTHER POTENTIAL METHODS FOR SEGMENTATION OF MICROORGANIMS <s> Recently, generative adversarial networks exhibited excellent performances in semi-supervised image analysis scenarios. In this paper, we go even further by proposing a fully unsupervised approach for segmentation applications with prior knowledge of the objects' shapes. We propose and investigate different strategies to generate simulated label data and perform image-to-image translation between the image and the label domain using an adversarial model. Specifically, we assess the impact of the annotation model's accuracy as well as the effect of simulating additional low-level image features. For experimental evaluation, we consider the segmentation of the glomeruli, an application scenario from renal pathology. Experiments provide proof of concept and also confirm that the strategy for creating the simulated label data is of particular relevance considering the stability of GAN trainings. <s> BIB009
Generative adversarial networks (GANs) based segmentation methods have shown a great potential in segmentation of microscopic images, which share similar segmentation characteristics as microorganisms. GAN is a machine learning network having two models which work in adversarial manner  . It is composed of a generative model that captures the data distribution and a discriminative model that estimates the probability that a sample came from training data rather than generator  . The generative network training objective is to increase the error rate of the discriminative network BIB002 . The architectural constraints of GANs have demonstrated that they are strong candidates for unsupervised learning. In the context of unsupervised learning, generative adversarial network (GAN) can be trained on unlimited amount of unlabelled images to learn their intermediate representations BIB001 . Incorporating them into image segmentation tasks for microscopic images, has shown great improvement in segmentation results. Some successful applications of GANs to microscopic images segmentation tasks are briefly introduced below: Motivated by the fact that, image translation shown by GAN loss can improve the blurred quality of image to image translation generated by U-net BIB003 , a dual contour adversarial network (DCANet) is used for segmentation of nucleii from pathological images of cancer tissue in BIB007 . In this network, a generator deploys two decoding lines which generate two image masks with different boundary transformation, then both images are post-processed using maker watershed before combining them to form a segmented image. Comparing with linkNet, FnsNet and Pix2Pix on segmentation of the same dataset, the proposed method outperforms with F1 score of 70.00%. A deep adversarial network for biomedical image segmentation (utilizing unannotated images) is applied on stained colon tissue images BIB004 , for glands segmentation. An evaluation convolution network (VGG-16 model) is encouraged to distinguish between segmentation results of unannotated images and annotated ones (by giving them different scores). While a segmentation network is encouraged to produce segmentation results of unannotated images such that evaluation network cannot distinguish them from the annotated ones. In BIB005 , a GAN is used in synthesizing data for aggressive prostate cancer detection, to tackle the scarcity of data set for training the fully convolutional network (FCN). The generator of GAN consists of the U-net-type architecture. It uses InsanceNorm layers instead of BatchNorm layers, conjecturing that it avoids harmful stochasticity introduced by small batch-sizes. The discriminator is similar to encoder of the generator. A sensitivity of 98.00% is achieved in segmentation. A convolutional network which works in the principle of GAN is applied to segment proteomics of individual cancer cells in BIB008 . Unlike the original GAN, this network doesn't generate images from random noise vectors, rather estimates the underlying variables of an image. The estimator learns to output some segmentation of the image while the discriminator learns to distinguish between expert manual segmentations and estimated segmentations given the associated image. (The estimator net is a five layer fully CNN, each layer is constructed of a convolution followed by batch normalization and leaky-ReLU activation. The discriminator is designed as three consecutive ''Rib Cage'' blocks followed by two fully-connected (FC) layers with leaky-ReLU activations and a final FC layer with one output and a sigmoid activation for classification). With this network the loss function is automatically defined as it is learned along side the estimator, making this a simple to use algorithm with no tuning necessity and it is robust to low number of training samples. The final segmentation result of 89.90% precision is achieved. Generally, similar approaches based on GANs can be applied in segmentation of microorganisms. Since they share some similar segmentation challenges with cancer cells, such as uneven illumination of backgrounds which are the results of poor staining of samples BIB006 , BIB007 , poor contrast BIB009 and scarcity of dataset BIB005 .