Style Consistent Image Generation for Nuclei Instance Segmentation

Gong, Xuan; Chen, Shuyan; Zhang, Baochang; Doermann, David

doi:10.1109/wacv48630.2021.00404

Cited by 23 publications

(9 citation statements)

References 31 publications

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“…Table 3 shows various performance measures found in this survey research. In cases, Brain: Retina: [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54] Microscopic: [55][56][57][58][59][60][61][62][63][64] Multi-Organs: [65][66][67][68][69][70][71] Liver: [72][73][74][75][76][77] Skin: [78][79][80][81][82][83] Orthopedic: [84][85][86][87] Cardiac: [88]…”

Section: Performance Metricsmentioning

confidence: 99%

“…This dataset consists of a total of 660 RGB images and is used in the research [115]. Similarly, MIVIA, ssTEM, SCD RBC, triple negative breast cancer (TNBC) are microscopy datasets that are used in GAN applications [55,116,132]. The MICCAI challenge 2015 provided the Gland Challenge dataset based on H&E-stained histology images.…”

Section: Datasets Based On Rgb Imagingmentioning

confidence: 99%

“…In the 2018 challenge, a multi-organ nuclei segmentation challenge was conducted, and microscopy images were provided. These challenging datasets are followed by the subsequent researchers [55,56].…”

Section: Datasets Based On Rgb Imagingmentioning

confidence: 99%

“…Microscopy studies: [55][56][57][58][59][60][61][62][63][64], [107], [115], [116], [122]-124], [132] Funduscopy studies: [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54], [121], [135], [138] Dermoscopy studies: [78][79][80][81][82][83], [118][119], [144] [29], is introduced to address the computational burden. Attention module is also proposed to select global information from the segmentor network.…”

Section: Rgb Imagingmentioning

confidence: 99%

“…Results are qualitatively and quantitatively evaluated against segmentation datasets. Gong et al [55] used Style Consistent GAN for the nuclei segmentation task. Furthermore, the model is used to segment real and fake nuclei images, which benefits the generator part to boost segmentation accuracy and performance.…”

Section: Gans Applications In Microscopic Segmentationmentioning

confidence: 99%

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Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey

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Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.

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