mlsanityData to 7 Oct 2026

Deep Learning for Pancreas Segmentation: a Systematic Review

arXiv 2407.16313 ↗First posted 23 Jul 2024eess.IV cs.CV cs.LGcited by 4

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Abstract (arXiv)

Pancreas segmentation has been traditionally challenging due to its small size in computed tomography abdominal volumes, high variability of shape and positions among patients, and blurred boundaries due to low contrast between the pancreas and surrounding organs. Many deep learning models for pancreas segmentation have been proposed in the past few years. We present a thorough systematic review based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. The literature search was conducted on PubMed, Web of Science, Scopus, and IEEE Xplore on original studies published in peer-reviewed journals from 2013 to 2023. Overall, 130 studies were retrieved. We initially provided an overview of the technical background of the most common network architectures and publicly available datasets. Then, the analysis of the studies combining visual presentation in tabular form and text description was reported. The tables grouped the studies specifying the application, dataset size, design (model architecture, learning strategy, and loss function), results, and main contributions. We first analyzed the studies focusing on parenchyma segmentation using coarse-to-fine approaches, multi-organ segmentation, semi-supervised learning, and unsupervised learning, followed by those studies on generalization to other datasets and those concerning the design of new loss functions. Then, we analyzed the studies on segmentation of tumors, cysts, and inflammation reporting multi-stage methods, semi-supervised learning, generalization to other datasets, and design of new loss functions. Finally, we provided a critical discussion on the subject based on the published evidence underlining current issues that need to be addressed before clinical translation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 37 of its 201 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

37 of 201 references.

References · 37

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25 more
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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2025Conformal Lesion Segmentation for 3D Medical Imagescited by 4
  2. 2025Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approachescited by 2
  3. 2024MiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Classification and Detectioncited by 1
  4. 2025Deep Learning Framework for Early Detection of Pancreatic Cancer Using Multi-Modal Medical Imaging Analysiscited by 0