Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping
Not on the map. Why?
Abstract (arXiv)
Designing deep learning algorithms for gland segmentation is crucial for automatic cancer diagnosis and prognosis, yet the expensive annotation cost hinders the development and application of this technology. In this paper, we make a first attempt to explore a deep learning method for unsupervised gland segmentation, where no manual annotations are required. Existing unsupervised semantic segmentation methods encounter a huge challenge on gland images: They either over-segment a gland into many fractions or under-segment the gland regions by confusing many of them with the background. To overcome this challenge, our key insight is to introduce an empirical cue about gland morphology as extra knowledge to guide the segmentation process. To this end, we propose a novel Morphology-inspired method via Selective Semantic Grouping. We first leverage the empirical cue to selectively mine out proposals for gland sub-regions with variant appearances. Then, a Morphology-aware Semantic Grouping module is employed to summarize the overall information about the gland by explicitly grouping the semantics of its sub-region proposals. In this way, the final segmentation network could learn comprehensive knowledge about glands and produce well-delineated, complete predictions. We conduct experiments on GlaS dataset and CRAG dataset. Our method exceeds the second-best counterpart over 10.56% at mIOU.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 25 of its 39 references that are in the map. First posted on arXiv 22 Jul 2023.
Builds on
25 of 39 references.
References · 25
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
- 2016Pyramid Scene Parsing Networkcited by 2,472
- 2018Unsupervised Feature Learning via Non-Parametric Instance-level Discriminationcited by 1,838
- 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,020
- 2018Invariant Information Clustering for Unsupervised Image Classification and Segmentationcited by 454
- 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
- 2022Unsupervised Semantic Segmentation by Distilling Feature Correspondencescited by 241
- 2016Gland Segmentation in Colon Histology Images: The GlaS Challenge Contestcited by 203
- 2021Unsupervised Semantic Segmentation by Contrasting Object Mask Proposalscited by 190
- 2019Deep neural network models for computational histopathology: A surveycited by 165
- 2021PiCIE: Unsupervised Semantic Segmentation using Invariance and Equivariance in Clusteringcited by 163
13 more
- 2022Deep Spectral Methods: A Surprisingly Strong Baseline for Unsupervised Semantic Segmentation and Localizationcited by 150
- 2016Rotation equivariant vector field networkscited by 98
- 2019SegSort: Segmentation by Discriminative Sorting of Segmentscited by 96
- 2018MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Imagescited by 87
- 2016DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentationcited by 84
- 2020Contrastive Registration for Unsupervised Medical Image Segmentationcited by 19
- 2021TA-Net: Topology-Aware Network for Gland Segmentationcited by 17
- 2022Online Easy Example Mining for Weakly-supervised Gland Segmentation from Histology Imagescited by 15
- 2021A Spatial Guided Self-supervised Clustering Network for Medical Image Segmentationcited by 14
- 2016Gland Instance Segmentation Using Deep Multichannel Neural Networkscited by 11
- 2022ConCL: Concept Contrastive Learning for Dense Prediction Pre-training in Pathology Imagescited by 11
- 2018BoxNet: Deep Learning Based Biomedical Image Segmentation Using Boxes Only Annotationcited by 3
- 2013PCG-Cut: Graph Driven Segmentation of the Prostate Central Glandcited by 2
Built on by
Not cited yet.