Anatomy-guided Pathology Segmentation
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916315803942912 |
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| author | Jaus, Alexander Seibold, Constantin Reiß, Simon Heine, Lukas Schily, Anton Kim, Moon Bahnsen, Fin Hendrik Herrmann, Ken Stiefelhagen, Rainer Kleesiek, Jens |
| author_facet | Jaus, Alexander Seibold, Constantin Reiß, Simon Heine, Lukas Schily, Anton Kim, Moon Bahnsen, Fin Hendrik Herrmann, Ken Stiefelhagen, Rainer Kleesiek, Jens |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_05844 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Anatomy-guided Pathology Segmentation Jaus, Alexander Seibold, Constantin Reiß, Simon Heine, Lukas Schily, Anton Kim, Moon Bahnsen, Fin Hendrik Herrmann, Ken Stiefelhagen, Rainer Kleesiek, Jens Computer Vision and Pattern Recognition 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. |
| title | Anatomy-guided Pathology Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.05844 |