SegBook：體積醫學影像分割的簡單基準和指南

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

計算斷層掃描（CT）是醫學影像學中最受歡迎的模式之一。到目前為止，CT影像為體積醫學分割任務提供了最大的公開數據集，涵蓋全身解剖結構。大量的全身CT影像為預先訓練強大的模型（例如以監督方式預先訓練的STU-Net）提供了機會，以分割眾多解剖結構。然而，目前尚不清楚這些預先訓練模型在何種條件下可以轉移到各種下游醫學分割任務，特別是分割其他模式和不同目標。為解決這個問題，需要一個大規模的綜合評估基準，以找出這些條件。因此，我們收集了87個不同模式、目標和樣本大小的公共數據集，以評估全身CT預先訓練模型的轉移能力。然後，我們使用了一個代表性模型STU-Net，搭配多個模型尺度，進行跨模式和目標的轉移學習。我們的實驗結果顯示：（1）在微調中可能存在有關數據集大小的瓶頸效應，相較於中等規模數據集，對於小型和大型數據集的改進更為明顯。 （2）在全身CT上預先訓練的模型展現出有效的模式轉移，能夠良好地適應其他模式，如MRI。 （3）在全身CT上進行預訓練不僅支持結構檢測的強大表現，還在病變檢測方面表現出效力，展示了跨目標任務的適應性。我們希望這個大規模的轉移學習開放評估能夠引導未來體積醫學影像分割研究。

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.