title: SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image
    Segmentation
summary: Computed Tomography (CT) is one of the most popular modalities for medical
  imaging. By far, CT images have contributed to the largest publicly available
  datasets for volumetric medical segmentation tasks, covering full-body
  anatomical structures. Large amounts of full-body CT images provide the
  opportunity to pre-train powerful models, e.g., STU-Net pre-trained in a
  supervised fashion, to segment numerous anatomical structures. However, it
  remains unclear in which conditions these pre-trained models can be transferred
  to various downstream medical segmentation tasks, particularly segmenting the
  other modalities and diverse targets. To address this problem, a large-scale
  benchmark for comprehensive evaluation is crucial for finding these conditions.
  Thus, we collected 87 public datasets varying in modality, target, and sample
  size to evaluate the transfer ability of full-body CT pre-trained models. We
  then employed a representative model, STU-Net with multiple model scales, to
  conduct transfer learning across modalities and targets. Our experimental
  results show that (1) there may be a bottleneck effect concerning the dataset
  size in fine-tuning, with more improvement on both small- and large-scale
  datasets than medium-size ones. (2) Models pre-trained on full-body CT
  demonstrate effective modality transfer, adapting well to other modalities such
  as MRI. (3) Pre-training on the full-body CT not only supports strong
  performance in structure detection but also shows efficacy in lesion detection,
  showcasing adaptability across target tasks. We hope that this large-scale open
  evaluation of transfer learning can direct future research in volumetric
  medical image segmentation.
paper_authors._id: 674549961934982716d2233c
paper_authors.name: Jin Ye
paper_authors._id: 674549961934982716d2233d
paper_authors.name: Ying Chen
paper_authors._id: 674549961934982716d2233e
paper_authors.name: Yanjun Li
paper_authors.status: admin_assigned
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paper_authors.fullname: Yanjun Li
paper_authors.type: user
paper_authors.user: ballinli
paper_authors._id: 674549961934982716d2233f
paper_authors.name: Haoyu Wang
paper_authors.status: admin_assigned
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paper_authors.fullname: Haoyu Wang
paper_authors.type: user
paper_authors.user: haoyuwang6
paper_authors._id: 674549961934982716d22340
paper_authors.name: Zhongying Deng
paper_authors.status: admin_assigned
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paper_authors.fullname: Zhongying Deng
paper_authors.type: user
paper_authors.user: zdcam294
paper_authors._id: 674549961934982716d22341
paper_authors.name: Ziyan Huang
paper_authors.status: admin_assigned
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paper_authors._id: 643fddd6388f31f64ccb9344
paper_authors.avatarUrl: /avatars/c029b59cfbbc4124489e72fa8e71ee9a.svg
paper_authors.fullname: Ziyan Huang
paper_authors.type: user
paper_authors.user: ziyanhuang
paper_authors._id: 674549961934982716d22342
paper_authors.name: Yanzhou Su
paper_authors._id: 674549961934982716d22343
paper_authors.name: Chenglong Ma
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:50:55.842Z
paper_authors._id: 666432a35adc67db967560d3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/u7-8F9-aH7uDAPgEYq12E.jpeg
paper_authors.fullname: Chenglong Ma
paper_authors.type: user
paper_authors.user: ChenglongMa
paper_authors._id: 674549961934982716d22344
paper_authors.name: Yuanfeng Ji
paper_authors._id: 674549961934982716d22345
paper_authors.name: Junjun He
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-11-26T22:50:50.224Z
paper_authors._id: 66b593026cef22e6ba6adb8a
paper_authors.avatarUrl: /avatars/8a94d55b85177e84d65dd0bd537e335f.svg
paper_authors.fullname: JunjunHe
paper_authors.type: user
paper_authors.user: JunjunHe
paper_title: SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image
    Segmentation
paper_summary: Computed Tomography (CT) is one of the most popular modalities for medical
  imaging. By far, CT images have contributed to the largest publicly available
  datasets for volumetric medical segmentation tasks, covering full-body
  anatomical structures. Large amounts of full-body CT images provide the
  opportunity to pre-train powerful models, e.g., STU-Net pre-trained in a
  supervised fashion, to segment numerous anatomical structures. However, it
  remains unclear in which conditions these pre-trained models can be transferred
  to various downstream medical segmentation tasks, particularly segmenting the
  other modalities and diverse targets. To address this problem, a large-scale
  benchmark for comprehensive evaluation is crucial for finding these conditions.
  Thus, we collected 87 public datasets varying in modality, target, and sample
  size to evaluate the transfer ability of full-body CT pre-trained models. We
  then employed a representative model, STU-Net with multiple model scales, to
  conduct transfer learning across modalities and targets. Our experimental
  results show that (1) there may be a bottleneck effect concerning the dataset
  size in fine-tuning, with more improvement on both small- and large-scale
  datasets than medium-size ones. (2) Models pre-trained on full-body CT
  demonstrate effective modality transfer, adapting well to other modalities such
  as MRI. (3) Pre-training on the full-body CT not only supports strong
  performance in structure detection but also shows efficacy in lesion detection,
  showcasing adaptability across target tasks. We hope that this large-scale open
  evaluation of transfer learning can direct future research in volumetric
  medical image segmentation.
paper_ai_keywords: STU-Net volumetric medical segmentation full-body CT images cross-modal transfer modality transfer lesion detection dataset size fine-tuning bottleneck effect structure detection pre-trained models adaptation across target tasks