PARASIDE: An Automatic Paranasal Sinus Segmentation and Structure Analysis Tool for MRI
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915120193470464 |
|---|---|
| author | Möller, Hendrik Krautschick, Lukas Atad, Matan Graf, Robert Busch, Chia-Jung Beule, Achim Scharf, Christian Kaderali, Lars Menze, Bjoern Rueckert, Daniel Kirschke, Jan Schwitzing, Fabian |
| author_facet | Möller, Hendrik Krautschick, Lukas Atad, Matan Graf, Robert Busch, Chia-Jung Beule, Achim Scharf, Christian Kaderali, Lars Menze, Bjoern Rueckert, Daniel Kirschke, Jan Schwitzing, Fabian |
| contents | Chronic rhinosinusitis (CRS) is a common and persistent sinus imflammation that affects 5 - 12\% of the general population. It significantly impacts quality of life and is often difficult to assess due to its subjective nature in clinical evaluation. We introduce PARASIDE, an automatic tool for segmenting air and soft tissue volumes of the structures of the sinus maxillaris, frontalis, sphenodalis and ethmoidalis in T1 MRI. By utilizing that segmentation, we can quantify feature relations that have been observed only manually and subjectively before. We performed an exemplary study and showed both volume and intensity relations between structures and radiology reports. While the soft tissue segmentation is good, the automated annotations of the air volumes are excellent. The average intensity over air structures are consistently below those of the soft tissues, close to perfect separability. Healthy subjects exhibit lower soft tissue volumes and lower intensities. Our developed system is the first automated whole nasal segmentation of 16 structures, and capable of calculating medical relevant features such as the Lund-Mackay score. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14514 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PARASIDE: An Automatic Paranasal Sinus Segmentation and Structure Analysis Tool for MRI Möller, Hendrik Krautschick, Lukas Atad, Matan Graf, Robert Busch, Chia-Jung Beule, Achim Scharf, Christian Kaderali, Lars Menze, Bjoern Rueckert, Daniel Kirschke, Jan Schwitzing, Fabian Computer Vision and Pattern Recognition Machine Learning Chronic rhinosinusitis (CRS) is a common and persistent sinus imflammation that affects 5 - 12\% of the general population. It significantly impacts quality of life and is often difficult to assess due to its subjective nature in clinical evaluation. We introduce PARASIDE, an automatic tool for segmenting air and soft tissue volumes of the structures of the sinus maxillaris, frontalis, sphenodalis and ethmoidalis in T1 MRI. By utilizing that segmentation, we can quantify feature relations that have been observed only manually and subjectively before. We performed an exemplary study and showed both volume and intensity relations between structures and radiology reports. While the soft tissue segmentation is good, the automated annotations of the air volumes are excellent. The average intensity over air structures are consistently below those of the soft tissues, close to perfect separability. Healthy subjects exhibit lower soft tissue volumes and lower intensities. Our developed system is the first automated whole nasal segmentation of 16 structures, and capable of calculating medical relevant features such as the Lund-Mackay score. |
| title | PARASIDE: An Automatic Paranasal Sinus Segmentation and Structure Analysis Tool for MRI |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.14514 |