Anatomy-guided Pathology Segmentation
This addresses the challenge of improving pathology segmentation accuracy for medical imaging applications, representing a novel integration of anatomical and pathological data rather than an incremental advance.
The paper tackles the problem of segmenting pathological structures in medical images by developing a generalist model that combines anatomical and pathological information, achieving state-of-the-art results with improvements of up to 3.3% on FDG-PET-CT and Chest X-Ray segmentation tasks.
Pathological structures in medical images are typically deviations from the expected anatomy of a patient. While clinicians consider this interplay between anatomy and pathology, recent deep learning algorithms specialize in recognizing either one of the two, rarely considering the patient's body from such a joint perspective. In this paper, we develop a generalist segmentation model that combines anatomical and pathological information, aiming to enhance the segmentation accuracy of pathological features. Our Anatomy-Pathology Exchange (APEx) training utilizes a query-based segmentation transformer which decodes a joint feature space into query-representations for human anatomy and interleaves them via a mixing strategy into the pathology-decoder for anatomy-informed pathology predictions. In doing so, we are able to report the best results across the board on FDG-PET-CT and Chest X-Ray pathology segmentation tasks with a margin of up to 3.3% as compared to strong baseline methods. Code and models will be publicly available at github.com/alexanderjaus/APEx.