A Multi-Organ Nucleus Segmentation Challenge

By Neeraj Kumar, Ruchika Verma, Deepak Anand, Yanning Zhou, O. Onder, Efstratios Tsougenis, Hao Chen, P. Heng, Jiahui Li, Yunzhi Wang and 76 more. IEEE Transactions on Medical Imaging, 2020

533 citations recorded by the source corpus.

Generalized nucleus segmentation techniques can contribute greatly to reducing the time to develop and validate visual biomarkers for new digital pathology datasets. We summarize the results of MoNuSeg 2018 Challenge whose objective was to develop generalizable nuclei segmentation techniques in digital pathology. The challenge was an official satellite event of the MICCAI 2018 conference in which 32 teams with more than 80 participants from geographically diverse institutes participated. Contestants were given a training set with 30 images from seven organs with annotations of 21,623 individual nuclei. A test dataset with 14 images taken from seven organs, including two organs that did not appear in the training set was released without annotations. Entries were evaluated based on average aggregated Jaccard index (AJI) on the test set to prioritize accurate instance segmentation as opposed to mere semantic segmentation. More than half the teams that completed the challenge outperformed a previous baseline. Among the trends observed that contributed to increased accuracy were the use of color normalization as well as heavy data augmentation. Additionally, fully convolutional networks inspired by variants of U-Net, FCN, and Mask-RCNN were popularly used, typically based on ResNet or VGG base architectures. Watershed segmentation on predicted semantic segmentation maps was a popular post-processing strategy. Several of the top techniques compared favorably to an individual human annotator and can be used with confidence for nuclear morphometrics.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/87c0dd990287d92796c7dc83edba6f52a2f52e21

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Citations in the corpus

Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, background): “A frequently encountered obstacle in medical imaging is that, in real-world applications, the acquired data 2D Microscope DSB[194] , MoNuSeg[195] Cross-patch dense contrastive learning framework Dt-DDCL, Zhang et al. [3] 2D Colonoscope kvasir-SEGdataset[196], Skin lesion dataset [197] Dual-task netw”
CellViT: Vision Transformers for Precise Cell Segmentation and Classification (2023, methodology): “The MoNuSeg[66, 67] dataset serves as an additional dataset for nuclei segmentation.”
SegDiff: Image Segmentation with Diffusion Probabilistic Models (2021, methodology): “The MoNuSeg dataset [25, 26] contains a training set with 30 microscopic images from seven organs, with annotations of 21,623 individual nuclei.”
UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer (2021, methodology): “It can be seen that the ‘ K 2 ’ and ‘ K 3 ’ have a more conﬁdent correlation with the other encoder level on the GlaS and MoNuSeg dataset, respectively.”
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].”
LViT: Language Meets Vision Transformer in Medical Image Segmentation (2022, methodology): “To validate the performance of LViT, we construct multimodal medical image segmentation datasets containing pathological images (MoNuSeg [9]) and X-rays (QaTa-COV19 [10]).”
Cellpose 2.0: how to train your own model (2022, methodology): “Briefly this dataset consisted of images from BBBC038v1 [52, 53], BBBC039v1 [52], MoNuSeg [54], and ISBI 2009 [55].”
Transformers in Medical Imaging: A Survey (2022, background): “Extensive experimentation on brain anatomy segmentation [160], gland segmentation [161], and MoNuSeg (microscopy) [162] demonstrate the effectiveness of their proposed gated axial attention module.”