mlsanityData to 7 Oct 2026

Generalized Organ Segmentation by Imitating One-shot Reasoning using Anatomical Correlation

arXiv 2103.16344 ↗First posted 30 Mar 2021cs.CVcited by 6

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Abstract (arXiv)

Learning by imitation is one of the most significant abilities of human beings and plays a vital role in human's computational neural system. In medical image analysis, given several exemplars (anchors), experienced radiologist has the ability to delineate unfamiliar organs by imitating the reasoning process learned from existing types of organs. Inspired by this observation, we propose OrganNet which learns a generalized organ concept from a set of annotated organ classes and then transfer this concept to unseen classes. In this paper, we show that such process can be integrated into the one-shot segmentation task which is a very challenging but meaningful topic. We propose pyramid reasoning modules (PRMs) to model the anatomical correlation between anchor and target volumes. In practice, the proposed module first computes a correlation matrix between target and anchor computerized tomography (CT) volumes. Then, this matrix is used to transform the feature representations of both anchor volume and its segmentation mask. Finally, OrganNet learns to fuse the representations from various inputs and predicts segmentation results for target volume. Extensive experiments show that OrganNet can effectively resist the wide variations in organ morphology and produce state-of-the-art results in one-shot segmentation task. Moreover, even when compared with fully-supervised segmentation models, OrganNet is still able to produce satisfying segmentation results.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 30 Mar 2021.

Builds on

17 of 31 references.

References · 17

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5 more
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Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 2.

6 papers.

  1. 2021Preservational Learning Improves Self-supervised Medical Image Models by Reconstructing Diverse Contextscited by 59
  2. 2023PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysiscited by 40
  3. 2023A Systematic Review of Few-Shot Learning in Medical Imagingcited by 28
  4. 2022Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registrationcited by 14
  5. 2022Unsupervised Tissue Segmentation via Deep Constrained Gaussian Networkcited by 6
  6. 2022Exemplar Learning for Medical Image Segmentationcited by 2