dorsal/arxiv
View SchemaRegion of interest detection for efficient aortic segmentation
| Authors | Loris Giordano, Ine Dirks, Tom Lenaerts, Jef Vandemeulebroucke |
|---|---|
| Categories | |
| ArXiv ID | 2601.08683vv1 |
| URL | https://arxiv.org/abs/2601.08683 |
| DOI | 10.1117/12.3046724 |
| Journal | Medical Imaging 2025: Image Processing (Vol. 13406, pp. 390-400). SPIE |
| License | http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Abstract
Thoracic aortic dissection and aneurysms are the most lethal diseases of the aorta. The major hindrance to treatment lies in the accurate analysis of the medical images. More particularly, aortic segmentation of the 3D image is often tedious and difficult. Deep-learning-based segmentation models are an ideal solution, but their inability to deliver usable outputs in difficult cases and their computational cost cause their clinical adoption to stay limited. This study presents an innovative approach for efficient aortic segmentation using targeted region of interest (ROI) detection. In contrast to classical detection models, we propose a simple and efficient detection model that can be widely applied to detect a single ROI. Our detection model is trained as a multi-task model, using an encoder-decoder architecture for segmentation and a fully connected network attached to the bottleneck for detection. We compare the performance of a one-step segmentation model applied to a complete image, nnU-Net and our cascade model composed of a detection and a segmentation step. We achieve a mean Dice similarity coefficient of 0.944 with over 0.9 for all cases using a third of the computing power. This simple solution achieves state-of-the-art performance while being compact and robust, making it an ideal solution for clinical applications.
{
"annotation_id": "5458d835-22f7-40a4-8af9-da420be49e3e",
"date_created": "2026-02-17T05:53:16.130000Z",
"date_modified": "2026-02-17T05:53:16.130000Z",
"file_hash": "38638b0435e8093ccdc16e4d859cfe0739ec6aa01bb275fd59332ced3dc152d4",
"private": false,
"record": {
"abstract": "Thoracic aortic dissection and aneurysms are the most lethal diseases of the aorta. The major hindrance to treatment lies in the accurate analysis of the medical images. More particularly, aortic segmentation of the 3D image is often tedious and difficult. Deep-learning-based segmentation models are an ideal solution, but their inability to deliver usable outputs in difficult cases and their computational cost cause their clinical adoption to stay limited. This study presents an innovative approach for efficient aortic segmentation using targeted region of interest (ROI) detection. In contrast to classical detection models, we propose a simple and efficient detection model that can be widely applied to detect a single ROI. Our detection model is trained as a multi-task model, using an encoder-decoder architecture for segmentation and a fully connected network attached to the bottleneck for detection. We compare the performance of a one-step segmentation model applied to a complete image, nnU-Net and our cascade model composed of a detection and a segmentation step. We achieve a mean Dice similarity coefficient of 0.944 with over 0.9 for all cases using a third of the computing power. This simple solution achieves state-of-the-art performance while being compact and robust, making it an ideal solution for clinical applications.",
"arxiv_id": "2601.08683",
"authors": [
"Loris Giordano",
"Ine Dirks",
"Tom Lenaerts",
"Jef Vandemeulebroucke"
],
"categories": [
"eess.IV",
"cs.AI",
"cs.CV"
],
"doi": "10.1117/12.3046724",
"journal_ref": "Medical Imaging 2025: Image Processing (Vol. 13406, pp. 390-400). SPIE",
"license": "http://creativecommons.org/licenses/by-nc-nd/4.0/",
"title": "Region of interest detection for efficient aortic segmentation",
"url": "https://arxiv.org/abs/2601.08683",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "eb5805c3-7565-4cb9-9b89-f1c940d8a055",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}