SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910523969241088 |
|---|---|
| author | Koitka, Sven Baldini, Giulia Schmidt, Cynthia S. Pollok, Olivia B. Pelka, Obioma Kohnke, Judith Borys, Katarzyna Friedrich, Christoph M. Schaarschmidt, Benedikt M. Forsting, Michael Umutlu, Lale Haubold, Johannes Nensa, Felix Hosch, René |
| author_facet | Koitka, Sven Baldini, Giulia Schmidt, Cynthia S. Pollok, Olivia B. Pelka, Obioma Kohnke, Judith Borys, Katarzyna Friedrich, Christoph M. Schaarschmidt, Benedikt M. Forsting, Michael Umutlu, Lale Haubold, Johannes Nensa, Felix Hosch, René |
| contents | Traditional segmentation networks approach anatomical structures as standalone elements, overlooking the intrinsic hierarchical connections among them. This study introduces Softmax for Arbitrary Label Trees (SALT), a novel approach designed to leverage the hierarchical relationships between labels, improving the efficiency and interpretability of the segmentations.
This study introduces a novel segmentation technique for CT imaging, which leverages conditional probabilities to map the hierarchical structure of anatomical landmarks, such as the spine's division into lumbar, thoracic, and cervical regions and further into individual vertebrae. The model was developed using the SAROS dataset from The Cancer Imaging Archive (TCIA), comprising 900 body region segmentations from 883 patients. The dataset was further enhanced by generating additional segmentations with the TotalSegmentator, for a total of 113 labels. The model was trained on 600 scans, while validation and testing were conducted on 150 CT scans. Performance was assessed using the Dice score across various datasets, including SAROS, CT-ORG, FLARE22, LCTSC, LUNA16, and WORD.
Among the evaluated datasets, SALT achieved its best results on the LUNA16 and SAROS datasets, with Dice scores of 0.93 and 0.929 respectively. The model demonstrated reliable accuracy across other datasets, scoring 0.891 on CT-ORG and 0.849 on FLARE22. The LCTSC dataset showed a score of 0.908 and the WORD dataset also showed good performance with a score of 0.844.
SALT used the hierarchical structures inherent in the human body to achieve whole-body segmentations with an average of 35 seconds for 100 slices. This rapid processing underscores its potential for integration into clinical workflows, facilitating the automatic and efficient computation of full-body segmentations with each CT scan, thus enhancing diagnostic processes and patient care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_08878 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees Koitka, Sven Baldini, Giulia Schmidt, Cynthia S. Pollok, Olivia B. Pelka, Obioma Kohnke, Judith Borys, Katarzyna Friedrich, Christoph M. Schaarschmidt, Benedikt M. Forsting, Michael Umutlu, Lale Haubold, Johannes Nensa, Felix Hosch, René Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Traditional segmentation networks approach anatomical structures as standalone elements, overlooking the intrinsic hierarchical connections among them. This study introduces Softmax for Arbitrary Label Trees (SALT), a novel approach designed to leverage the hierarchical relationships between labels, improving the efficiency and interpretability of the segmentations. This study introduces a novel segmentation technique for CT imaging, which leverages conditional probabilities to map the hierarchical structure of anatomical landmarks, such as the spine's division into lumbar, thoracic, and cervical regions and further into individual vertebrae. The model was developed using the SAROS dataset from The Cancer Imaging Archive (TCIA), comprising 900 body region segmentations from 883 patients. The dataset was further enhanced by generating additional segmentations with the TotalSegmentator, for a total of 113 labels. The model was trained on 600 scans, while validation and testing were conducted on 150 CT scans. Performance was assessed using the Dice score across various datasets, including SAROS, CT-ORG, FLARE22, LCTSC, LUNA16, and WORD. Among the evaluated datasets, SALT achieved its best results on the LUNA16 and SAROS datasets, with Dice scores of 0.93 and 0.929 respectively. The model demonstrated reliable accuracy across other datasets, scoring 0.891 on CT-ORG and 0.849 on FLARE22. The LCTSC dataset showed a score of 0.908 and the WORD dataset also showed good performance with a score of 0.844. SALT used the hierarchical structures inherent in the human body to achieve whole-body segmentations with an average of 35 seconds for 100 slices. This rapid processing underscores its potential for integration into clinical workflows, facilitating the automatic and efficient computation of full-body segmentations with each CT scan, thus enhancing diagnostic processes and patient care. |
| title | SALT: Introducing a Framework for Hierarchical Segmentations in Medical Imaging using Softmax for Arbitrary Label Trees |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.08878 |