在类别不平衡的CT身体成分分割中解耦采样与训练预算

摘要

类别不平衡是医学图像分割中的一个基本挑战，常见类别通常会主导训练过程，而稀有类别则被忽视。基于损失函数的方法通过重新加权批次内逐像素损失来缓解不平衡，而采样策略则控制哪些图像进入批次。然而，这两种方法均未明确控制批次中出现的类别，导致稀有类别的暴露只能部分得到平衡。在本工作中，我们采用少样本学习中的情景采样，在全监督设置下促进类别平衡的批次构建。我们将情景采样与传统度量学习背景解耦，并在CT身体成分分割中对其进行评估。我们基于公共SAROS数据集中的210次扫描，在九种肌肉和脂肪组织上比较了情景采样与随机采样及加权采样。训练在完整数据和低数据两种场景下进行，并在匹配训练迭代预算下进行额外比较。在完整数据训练下，三种策略表现相当（情景采样平均Dice为0.882，随机和加权采样均为0.878）。在低数据训练下，情景采样优于随机和加权采样（0.787对比0.758和0.762），这一优势源于训练迭代次数相差12倍。在匹配训练预算下，随机和加权采样更早出现过拟合，而情景采样在收敛前持续改进约三倍迭代次数。我们的研究发现训练迭代预算作为采样策略中未被充分认识的混淆因素，推动了针对小数据集的迭代感知评估协议。此外，情景采样的残余优势与类别平衡批次的隐式正则化效应一致，为类别不平衡的医学图像分割提供了一种低成本、模型无关的策略。代码见 https://github.com/iasonsky/episodic-sampling。

English

Class imbalance is a fundamental challenge in medical image segmentation, where frequent classes typically dominate training at the expense of rare classes. Loss-based approaches mitigate imbalance by reweighting the per-pixel loss within the batch, while sampling strategies control which images enter the batch. Yet neither explicitly controls which classes appear within the batch, leaving rare-class exposure only partially rebalanced. In this work, we adopt episodic sampling from few-shot learning to promote class-balanced batch construction in a fully supervised setting. We decouple episodic sampling from its conventional metric-learning context and evaluate it in body composition segmentation in CT. We compare episodic sampling against random and weighted sampling on nine muscle and adipose tissues, derived from 210 scans of the public SAROS dataset. Training is performed under full- and low-data regimes, with additional comparisons under matched training iteration budgets. Under full-data training, all three strategies performed comparably (mean Dice 0.882 for episodic, 0.878 for random and weighted). Under low-data training, episodic sampling outperformed random and weighted (0.787 vs. 0.758 and 0.762), driven by a 12-fold difference in training iterations. Under matched training budgets, random and weighted overfit earlier, while episodic improved for approximately three times more iterations before plateauing. Our findings identify the training iteration budget as under-recognized confound in sampling strategies, motivating iteration-aware evaluation protocols for small datasets. Furthermore, the residual advantage of episodic sampling is consistent with an implicit regularization effect of class-balanced batches, offering a low-cost, model-agnostic strategy for class-imbalanced medical image segmentation. Code is available at https://github.com/iasonsky/episodic-sampling.