SegBook：用于体积医学图像分割的简单基线和指南

SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation

November 21, 2024

作者: Jin Ye, Ying Chen, Yanjun Li, Haoyu Wang, Zhongying Deng, Ziyan Huang, Yanzhou Su, Chenglong Ma, Yuanfeng Ji, Junjun He

cs.AI

摘要

计算机断层扫描（CT）是医学成像中最流行的模态之一。迄今为止，CT图像为体积医学分割任务提供了最大的公开数据集，涵盖全身解剖结构。大量的全身CT图像为预训练强大模型（例如以监督方式预训练的STU-Net）提供了机会，用于分割多种解剖结构。然而，目前尚不清楚这些预训练模型在哪些条件下可以转移到不同的下游医学分割任务，特别是分割其他模态和多样化目标。为解决这一问题，对于找到这些条件，关键是进行大规模基准测试。因此，我们收集了87个不同模态、目标和样本大小的公共数据集，以评估全身CT预训练模型的迁移能力。然后，我们采用了代表性模型STU-Net，并使用多个模型尺度进行模态和目标之间的迁移学习。我们的实验结果显示：（1）在微调中可能存在有关数据集大小的瓶颈效应，对于小型和大型数据集的改进要比中等规模数据集更多；（2）在全身CT上预训练的模型展现出有效的模态转移能力，能够很好地适应其他模态，如MRI；（3）在全身CT上进行预训练不仅支持强大的结构检测性能，而且在病变检测方面也表现出有效性，展示了跨目标任务的适应能力。我们希望这种大规模开放的迁移学习评估能够引导未来体积医学图像分割的研究方向。

English

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.