Spheroid Segmentation Using Multiscale Deep Adversarial Networks

Sadanandan, Sajith Kecheril; Karlsson, Johan; Wählby, Carolina

doi:10.1109/iccvw.2017.11

Cited by 15 publications

(17 citation statements)

References 6 publications

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“…Sadanandan et al , showed that the dimensions of U‐Net could be reduced by combining raw images with images pre‐filtered with task specific hand engineered filters, achieving robust segmentation of Escherichia coli and mouse mammary cells in both phase contrast and fluorescence images. In another article Sadanandan et al combined CNNs and GANs for segmenting spheroid cell clusters in bright field images. Rather than forcing the GAN to re‐create synthetic images, it was recursively used to improve a set of manually drawn segmentation masks, achieving performance gains over a baseline CNN segmentation architecture.…”

Section: Deep Learning For Image Cytometrymentioning

confidence: 99%

Deep Learning in Image Cytometry: A Review

et al. 2018

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Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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Section: Deep Learning For Image Cytometrymentioning

confidence: 99%

Deep Learning in Image Cytometry: A Review

et al. 2018

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“…This differs from aforementioned works in the sense that the additional loss term is being learned by the discriminator rather than having fixed hand-crafted loss terms. The same mechanism was later applied to image-to-image translation [20], medical image analysis [5,6,7,8,16,21,22,23] and other segmentation tasks [24]. In contrast to our work, this formulation of adversarial training does not use the pairing information of images and labels.…”

Section: Related Workmentioning

confidence: 99%

EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

Ghafoorian

Nugteren

Baka

et al. 2019

Lecture Notes in Computer Science

170

128

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Convolutional neural networks have been successfully applied to semantic segmentation problems. However, there are many problems that are inherently not pixel-wise classification problems but are nevertheless frequently formulated as semantic segmentation. This ill-posed formulation consequently necessitates hand-crafted scenario-specific and computationally expensive post-processing methods to convert the per pixel probability maps to final desired outputs. Generative adversarial networks (GANs) can be used to make the semantic segmentation network output to be more realistic or better structure-preserving, decreasing the dependency on potentially complex post-processing. In this work, we propose EL-GAN: a GAN framework to mitigate the discussed problem using an embedding loss. With EL-GAN, we discriminate based on learned embeddings of both the labels and the prediction at the same time. This results in much more stable training due to having better discriminative information, benefiting from seeing both 'fake' and 'real' predictions at the same time. This substantially stabilizes the adversarial training process. We use the TuSimple lane marking challenge to demonstrate that with our proposed framework it is viable to overcome the inherent anomalies of posing it as a semantic segmentation problem. Not only is the output considerably more similar to the labels when compared to conventional methods, the subsequent post-processing is also simpler and crosses the competitive 96% accuracy threshold.

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“…Several methods have used GAN for microscopy segmentation [2,15,19]. [2] applied GAN by proposing a multiple input architecture in the discriminators of GAN, which takes as input both microscopy images and corresponding annotated segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…[2] applied GAN by proposing a multiple input architecture in the discriminators of GAN, which takes as input both microscopy images and corresponding annotated segmentation. [15] extended GAN by replacing the generative model with a multi-scale segmentation network. One limitation of these method, however, is that they still require manually annotated images during training.…”

Section: Related Workmentioning

confidence: 99%

“…There have been several applications to leverage GAN-based approaches in the microscopy segmentation field [2,15,19]. In [15], a multi-scale GAN was proposed with post processing for bright-field microscopy image segmentation, while [2] developed a GAN architecture with multiple deep neural network blocks in the discriminator. However, both methods were trained in a supervised fashion and still required manually labeled data.…”

Section: Introductionmentioning

confidence: 99%

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Cross Modality Microscopy Segmentation via Adversarial Adaptation

Guo

Wang

Krupa

et al. 2019

Bioinformatics and Biomedical Engineering

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Deep learning techniques have been successfully applied to automatically segment and quantify cell-types in images acquired from both confocal and light sheet fluorescence microscopy. However, the training of deep learning networks requires a massive amount of manually-labeled training data, which is a very time-consuming operation. In this paper, we demonstrate an adversarial adaptation method to transfer deep network knowledge for microscopy segmentation from one imaging modality (e.g., confocal) to a new imaging modality (e.g., light sheet) for which no or very limited labeled training data is available. Promising segmentation results show that the proposed transfer learning approach is an effective way to rapidly develop segmentation solutions for new imaging methods.

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Spheroid Segmentation Using Multiscale Deep Adversarial Networks

Cited by 15 publications

References 6 publications

Deep Learning in Image Cytometry: A Review

Deep Learning in Image Cytometry: A Review

EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection

Cross Modality Microscopy Segmentation via Adversarial Adaptation

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