mlsanityData to 14 Oct 2026

Towards more precise automatic analysis: a comprehensive survey of deep learning-based multi-organ segmentation

arXiv 2303.00232 ↗First posted 1 Mar 2023eess.IV cs.CVcited by 3

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Abstract (arXiv)

Accurate segmentation of multiple organs of the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation method has far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. For the first time, from the perspective of full and imperfect annotation, we comprehensively compile 161 studies on deep learning-based multi-organ segmentation in multiple regions such as the head and neck, chest, and abdomen, containing a total of 214 related references. The method based on full annotation summarizes the existing methods from four aspects: network architecture, network dimension, network dedicated modules, and network loss function. The method based on imperfect annotation summarizes the existing methods from two aspects: weak annotation-based methods and semi annotation-based methods. We also summarize frequently used datasets for multi-organ segmentation and discuss new challenges and new research trends in this field.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 94 of its 217 references that are in the map. First posted on arXiv 1 Mar 2023.

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94 of 217 references.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Deep Mutual Learning among Partially Labeled Datasets for Multi-Organ Segmentationcited by 2
  2. 2025MOSAIC: A Multi-View 2.5D Organ Slice Selector with Cross-Attentional Reasoning for Anatomically-Aware CT Localization in Medical Organ Segmentationcited by 1
  3. 2026Leveraging Transfer Learning with Class-Specific Decoders for Laparoscopic Segmentationcited by 0