Abstract
Multi-organ segmentation is a widely applied clinical routine and automated organ segmentation tools dramatically improve the pipeline of the radiologists. Recently, deep learning (DL) based segmentation models have shown the capacity to accomplish such a task. However, the training of the segmentation networks requires large amount of data with manual annotations, which is a major concern due to the data scarcity from clinic. Working with limited data is still common for researches on novel imaging modalities. To enhance the effectiveness of DL models trained with limited data, data augmentation (DA) is a crucial regularization technique. Traditional DA (TDA) strategies focus on basic intra-image operations, i.e. generating images with different orientations and intensity distributions. In contrast, the interimage and object-level DA operations are able to create new images from separate individuals. However, such DA strategies are not well explored on the task of multi-organ segmentation. In this paper, we investigated four possible inter-image DA strategies: CutMix, CarveMix, ObjectAug and AnatoMix, on two organ segmentation datasets. The result shows that CutMix, CarveMix and AnatoMix can improve the average dice score by 4.9, 2.0 and 1.9, compared with the state-of-the-art nnUNet without DA strategies. These results can be further improved by adding TDA strategies. It is revealed in our experiments that Cut-Mix is a robust but simple DA strategy to drive up the segmentation performance for multi-organ segmentation, even when CutMix produces intuitively 'wrong' images. Our implementation is publicly available for future benchmarks.
Abstract (translated)
多器官分割是一种广泛应用于临床的常规流程，而自动化的器官分割工具能够显著提升放射科医生的工作效率。近年来，基于深度学习（DL）的分割模型显示出完成此类任务的能力。然而，训练分割网络需要大量带有手动注释的数据，而这在临床上是一个重大问题，因为数据稀缺是普遍存在的。对于新型成像模式的研究工作，在有限数据的情况下仍是很常见的状况。为了增强使用有限数据训练的深度学习模型的效果，数据扩充（DA）是一种关键的技术手段。传统的数据扩充（TDA）策略主要集中在图像内部的基本操作上，比如生成不同方向和强度分布的图像。相比之下，跨图和对象级别的数据扩充操作能够从不同的个体创建新的图像。然而，在多器官分割任务中，这些数据扩充策略尚未被充分研究。 在本文中，我们对四种可能的跨图数据扩充策略：CutMix、CarveMix、ObjectAug 和 AnatoMix 在两个器官分割数据集上进行了调查。结果显示，与最先进的 nnUNet（未使用数据扩充策略）相比，CutMix、CarveMix 和 AnatoMix 可以分别将平均 Dice 系数提高 4.9、2.0 和 1.9。通过添加传统数据扩充策略，这些结果可以进一步改进。在我们的实验中发现，即使生成的图像从直观上看似乎是“错误”的，CutMix 是一种简单而稳健的数据扩充策略，能够显著提升多器官分割的表现。 我们的实现方案将公开提供给未来的研究和基准测试使用。
URL
https://arxiv.org/abs/2602.03555
