Data augmentation (DA) is a key factor in medical image analysis, such as in prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art computer-aided diagnosis systems still rely on simplistic spatial transformations to preserve the pathological label post transformation. However, such augmentations do not substantially increase the organ as well as tumor shape variability in the training set, limiting the model's ability to generalize to unseen cases with more diverse localized soft-tissue deformations. We propose a new anatomy-informed transformation that leverages information from adjacent organs to simulate typical physiological deformations of the prostate and generates unique lesion shapes without altering their label. Due to its lightweight computational requirements, it can be easily integrated into common DA frameworks. We demonstrate the effectiveness of our augmentation on a dataset of 774 biopsy-confirmed examinations, by evaluating a state-of-the-art method for PCa detection with different augmentation settings.

翻译：数据增强（DA）是医学图像分析（例如磁共振图像上的前列腺癌（PCa）检测）中的关键因素。最先进的计算机辅助诊断系统仍依赖简单的空间变换来保持变换后的病理标签。然而，此类增强并未显著增加训练集中器官及肿瘤形状的多样性，限制了模型对具有更多样化局部软组织变形的未见过病例的泛化能力。我们提出一种新的解剖学引导变换，利用相邻器官的信息模拟前列腺的典型生理变形，并生成独特的病灶形状而不改变其标签。由于其计算需求轻量，该变换可轻松集成到常见的DA框架中。我们通过评估不同增强设置下最先进的PCa检测方法，在774例经活检确认的检查数据集上验证了该增强的有效性。