Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes
Tianwei Zhang, Dong Wei, Mengmeng Zhu, Shi Gu, Yefeng Zheng
TL;DR
The paper tackles limited labeled data in medical image analysis by introducing two self-supervised pretext tasks that exploit anatomy-oriented imaging planes. It presents Relative Orientation Regression and Relative Location Regression, along with a multitask SSL variant, to pretrain models on cardiac and knee MRI data. Pretraining with these tasks significantly improves transfer to semantic segmentation and diagnostic classification, especially in low-data regimes, outperforming several SSL baselines and ImageNet-pretrained models. This anatomy-aware SSL approach reduces annotation needs and enhances practical medical imaging pipelines across multiple organs and tasks.
Abstract
Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The relevance between the pretraining pretext and target tasks is crucial to the success of transfer learning. Various pretext tasks have been proposed to utilize properties of medical image data (e.g., three dimensionality), which are more relevant to medical image analysis than generic ones for natural images. However, previous work rarely paid attention to data with anatomy-oriented imaging planes, e.g., standard cardiac magnetic resonance imaging views. As these imaging planes are defined according to the anatomy of the imaged organ, pretext tasks effectively exploiting this information can pretrain the networks to gain knowledge on the organ of interest. In this work, we propose two complementary pretext tasks for this group of medical image data based on the spatial relationship of the imaging planes. The first is to learn the relative orientation between the imaging planes and implemented as regressing their intersecting lines. The second exploits parallel imaging planes to regress their relative slice locations within a stack. Both pretext tasks are conceptually straightforward and easy to implement, and can be combined in multitask learning for better representation learning. Thorough experiments on two anatomical structures (heart and knee) and representative target tasks (semantic segmentation and classification) demonstrate that the proposed pretext tasks are effective in pretraining deep networks for remarkably boosted performance on the target tasks, and superior to other recent approaches.