Anatomy-guided Pathology Segmentation
<|reference_start|>Anatomy-guided Pathology Segmentation: 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.<|reference_end|>
@article{jaus2024anatomy-guided,
    title={Anatomy-guided Pathology Segmentation},
    author={Alexander Jaus, Constantin Seibold, Simon Rei{ss}, Lukas Heine, Anton
  Schily, Moon Kim, Fin Hendrik Bahnsen, Ken Herrmann, Rainer Stiefelhagen,
  Jens Kleesiek},
    journal={arXiv preprint arXiv:2407.05844},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05844},
    primaryClass={cs.CV}
}