SAM++: Enhancing Anatomic Matching using Semantic Information and
  Structural Inference
Xiaoyu Bai Yong Xia
Medical images like CT and MRI provide detailed information about the
internal structure of the body, and identifying key anatomical structures from
these images plays a crucial role in clinical workflows. Current methods treat
it as a registration or key-point regression task, which has limitations in
accurate matching and can only handle predefined landmarks. Recently, some
methods have been introduced to address these limitations. One such method,
called SAM, proposes using a dense self-supervised approach to learn a distinct
embedding for each point on the CT image and achieving promising results.
Nonetheless, SAM may still face difficulties when dealing with structures that
have similar appearances but different semantic meanings or similar semantic
meanings but different appearances. To overcome these limitations, we propose
SAM++, a framework that simultaneously learns appearance and semantic
embeddings with a novel fixed-points matching mechanism. We tested the SAM++
framework on two challenging tasks, demonstrating a significant improvement
over the performance of SAM and outperforming other existing methods.