Consensus Based Medical Image Segmentation Using Semi-Supervised Learning And Graph Cuts
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Abstract (arXiv)
Medical image segmentation requires consensus ground truth segmentations to be derived from multiple expert annotations. A novel approach is proposed that obtains consensus segmentations from experts using graph cuts (GC) and semi supervised learning (SSL). Popular approaches use iterative Expectation Maximization (EM) to estimate the final annotation and quantify annotator's performance. Such techniques pose the risk of getting trapped in local minima. We propose a self consistency (SC) score to quantify annotator consistency using low level image features. SSL is used to predict missing annotations by considering global features and local image consistency. The SC score also serves as the penalty cost in a second order Markov random field (MRF) cost function optimized using graph cuts to derive the final consensus label. Graph cut obtains a global maximum without an iterative procedure. Experimental results on synthetic images, real data of Crohn's disease patients and retinal images show our final segmentation to be accurate and more consistent than competing methods.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2016.
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Citing papers per year: 2021: 4; 2022: 4.
8 papers.
- 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentationcited by 22
- 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysiscited by 8
- 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentationcited by 5
- 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classificationcited by 5
- 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learningcited by 4
- 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classificationcited by 2
- 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformerscited by 1
- 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysiscited by 0
- 2022Cyclic Generative Adversarial Networks With Congruent Image-Report Generation For Explainable Medical Image Analysis16 Nov 2022
- 2022Improved Super Resolution of MR Images Using CNNs and Vision Transformers24 Jul 2022
- 2022Unsupervised Domain Adaptation Using Feature Disentanglement And GCNs For Medical Image Classification27 Jun 2022
- 2022Interpretable Saliency Maps And Self-Supervised Learning For Generalized Zero Shot Medical Image Classification4 Apr 2022
- 2021Multimodal Generalized Zero Shot Learning for Gleason Grading using Self-Supervised Learning15 Nov 2021
- 2021Learning of Inter-Label Geometric Relationships Using Self-Supervised Learning: Application To Gleason Grade Segmentation1 Oct 2021
- 2021CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis15 Jun 2021
- 2021Interpretability-Driven Sample Selection Using Self Supervised Learning For Disease Classification And Segmentation13 Apr 2021