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Abstract. Accurate colon segmentation on abdominal CT scans is crucial for various clinical applications. In this work, we propose an accurate AQ1 approach to colon segmentation from abdomen CT scans. Our architecture incorporates 3D contextual information via sequential episodic training (SET). In each episode, we used two consecutive slices, in a CT scan, as support and query samples in addition to other slices that did not include colon regions as negative samples. Choosing consecutive slices is a proper assumption for support and query samples, as the anatomy of the body does not have abrupt changes. Unlike traditional few-shot segmentation (FSS) approaches, we use the episodic training strategy in a supervised manner. In addition, to improve the discriminability of the learned features of the model, an embedding space is developed using contrastive learning. To guide the contrastive learning process, we use AQ2 an initial labeling that is generated by a Markov random field (MRF)- based approach. Finally, in the inference phase, we first detect the rec tum, which can be accurately extracted using the MRF-based approach, and then apply the SET on the remaining slices. Experiments on our private dataset of 98 CT scans and a public dataset of 30 CT scans illustrate that the proposed FSS model achieves a remarkable validation dice coefficient (DC) of 97.3% (Jaccard index, JD 94. 5%) compared to the classical FSS approaches 82.1% (JD 70.3%). Our findings highlight the efficacy of sequential episodic training in accurate 3D medical imaging segmentation. The codes for the proposed models are available at https://github.com/Samir-Farag/ICPR2024.
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MSI: Maximize Support-Set Information for Few-Shot Segmentation
FSS (Few-shot segmentation) aims to segment a target class using a small number of labeled images (support set). To extract information relevant to the target class, a dominant approach in best performing FSS methods removes background features using a support mask. We observe that this feature excision through a limiting support mask introduces an information bottleneck in several challenging FSS cases, e.g., for small targets and/or inaccurate target boundaries. To this end, we present a novel method (MSI), which maximizes the support-set information by exploiting two complementary sources of features to generate super correlation maps. We validate the effectiveness of our approach by instantiating it into three recent and strong FSS methods. Experimental results on several publicly available FSS benchmarks show that our proposed method consistently improves performance by visible margins and leads to faster convergence. Our code and trained models are available at: https://github.com/moonsh/MSI-Maximize-Support-Set-Information
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- Award ID(s):
- 1955365
- PAR ID:
- 10453084
- Date Published:
- Journal Name:
- International Conference on Computer Vision (ICCV)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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