bibtext: @InProceedings{ Jau_Anatomyguided_MICCAI2024,
                   author = { Jaus, Alexander and Seibold, Constantin and Reiß, Simon and Heine, Lukas and Schily, Anton and Kim, Moon and Bahnsen, Fin Hendrik and Herrmann, Ken and Stiefelhagen, Rainer and Kleesiek, Jens },
                   title = { { Anatomy-guided Pathology Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 are available at github.com/alexanderjaus/APEx.
title: Anatomy-guided Pathology Segmentation
authors: Jaus, Alexander Seibold, Constantin Reiß, Simon Heine, Lukas Schily, Anton Kim, Moon Bahnsen, Fin Hendrik Herrmann, Ken Stiefelhagen, Rainer Kleesiek, Jens