Gland segmentation in colon histology images: The glas challenge contest

By Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen, Xiaojuan Qi, P. Heng, Y. Guo, L. Wang, B. Matuszewski, Elia Bruni, Urko Sanchez and 9 more. Medical Image Anal., 2016

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&NA; Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter‐observer as well as intra‐observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods. HighlightsThe Gland Segmentation in Colon Histology Images Challenge (GlaS) Contest at MICCAI15.The complete details of the challenge are presented.The descriptions of the top performing methods are presented.Evaluation results of the top performing methods are presented. Graphical abstract Figure. No caption available.

Subfields: Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8ecc6ecd5b77665b261cce1a96d431d9f43bdaea

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Citations in the corpus

Automated Melanoma Recognition in Dermoscopy Images via Very Deep Residual Networks (2017, neutral): “This may be because this team focused on the segmentation task and employed some pre- and post- processing schemes to refine the results [43], whereas the main aim of our FCRN was to provide a good basis for the following recognition task and we did not employ any compelling refinement step.”
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation (2017, methodology): “Recent advances in deep learning [2,3,12,15,16] have achieved promising results on many biomedical image segmentation benchmarks [1,14].”
Machine Learning Methods for Histopathological Image Analysis (2017, background): “[23] Sirinukunwattana K, Pluim JPW, Chen H, Qi X, Heng P-A, Guo YB, et al.”
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, methodology): “[31] 2D Microscope Gland Segmentation Challenge dataset [32], ColoRectal Adenocarcinoma Gland (CRAG) [33] Add an error prediction network to divide segmentation errors into intra-class inconsistency or inter-class similarity problems online”
MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation (2019, methodology): “U-Net and U-Net like models have been successfully used in segmenting biomedical images of neuronal structures [24], liver [25], skin lesion [26], colon histology [27], kidney [28], vascular boundary [29], lung nodule [30], prostate [31], etc. and the list goes on.”
Applications of Deep Learning and Reinforcement Learning to Biological Data (2017, background): “reported the results of the Gland Segmentation competition from colon histology images [156].”
Medical Image Segmentation Review: The Success of U-Net (2022, methodology): “The UCTransNet network performs SOTA Dice results on the GlaS [148], MoNuSeg [149], [150] and Synapse [89] datasets in comparison with TransUNet [38].”
Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images (2020, methodology): “For gland segmentation, we employed the same quantitative measures that were used in the GlaS challenge [40].”
The Medical Segmentation Decathlon (2021, background): “Most of the observed task-specific performances reflect the natural difficulty and expected inter-rater variability of the tasks: Liver and spleen are large organs that are easy to detect and outline [33], whereas pancreas and colon cancers are much harder to segment as annotation experts themselves”
Dual-stream Multiple Instance Learning Network for Whole Slide Image Classification with Self-supervised Contrastive Learning (2020, background): “We have begun to see some recent success from computer vision and medical image analysis communities [14, 41, 48, 3], attributing to the advances in deep learning.”