ASA: Learning Anatomical Consistency, Sub-volume Spatial Relationships and Fine-Grained Appearance for CT Images

To achieve superior performance, deep learning relies on copiousness, high-quality, annotated data, but annotating medical images is tedious, laborious, and time-consuming, demanding specialized expertise, especially for segmentation tasks. Segmenting medical images requires not only macroscopic anatomical patterns but also microscopic textural details. Given the intriguing symmetry and recurrent patterns inherent in medical images, we envision a powerful deep model that exploits high-level context, spatial relationships in anatomy, and low-level, fine-grained, textural features in tissues in a self-supervised manner. To realize this vision, we have developed a novel self-supervised learning (SSL) approach called ASA to learn consistency, sub-volume relationships, and fine-grained for 3D computed tomography images. The novelty of ASA stems from its utilization of intrinsic properties of medical images, with a specific focus on computed tomography volumes. ASA enhances the model’s capability to learn anatomical features from the image, encompassing global representation, local spatial relationships, and intricate appearance details. Extensive experimental results validate the robustness, effectiveness, and efficiency of the pretrained ASA model. With all code and pretrained models released at GitHub.com/JLiangLab/ASA, we hope ASA serves as an inspiration and a foundation for developing enhanced SSL models with a deep understanding of anatomical structures and their spatial relationships, thereby improving diagnostic accuracy and facilitating advanced medical imaging applications.