TissUnet: Improved Extracranial Tissue and Cranium Segmentation for Children through Adulthood
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| Natura: | Preprint |
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2025
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| author | Mandzak, Markiian Yang, Elvira Zapaishchykova, Anna Chen, Yu-Hui Heilbroner, Lucas Zielke, John Tak, Divyanshu Mojahed-Yazdi, Reza Mussa, Francesca Romana Ye, Zezhong Vajapeyam, Sridhar Benitez, Viviana Salloum, Ralph Chi, Susan N. Sotoudeh, Houman Seidlitz, Jakob Mueller, Sabine Aerts, Hugo J. W. L. Poussaint, Tina Y. Kann, Benjamin H. |
| author_facet | Mandzak, Markiian Yang, Elvira Zapaishchykova, Anna Chen, Yu-Hui Heilbroner, Lucas Zielke, John Tak, Divyanshu Mojahed-Yazdi, Reza Mussa, Francesca Romana Ye, Zezhong Vajapeyam, Sridhar Benitez, Viviana Salloum, Ralph Chi, Susan N. Sotoudeh, Houman Seidlitz, Jakob Mueller, Sabine Aerts, Hugo J. W. L. Poussaint, Tina Y. Kann, Benjamin H. |
| contents | Extracranial tissues visible on brain magnetic resonance imaging (MRI) may hold significant value for characterizing health conditions and clinical decision-making, yet they are rarely quantified. Current tools have not been widely validated, particularly in settings of developing brains or underlying pathology. We present TissUnet, a deep learning model that segments skull bone, subcutaneous fat, and muscle from routine three-dimensional T1-weighted MRI, with or without contrast enhancement. The model was trained on 155 paired MRI-computed tomography (CT) scans and validated across nine datasets covering a wide age range and including individuals with brain tumors. In comparison to AI-CT-derived labels from 37 MRI-CT pairs, TissUnet achieved a median Dice coefficient of 0.79 [IQR: 0.77-0.81] in a healthy adult cohort. In a second validation using expert manual annotations, median Dice was 0.83 [IQR: 0.83-0.84] in healthy individuals and 0.81 [IQR: 0.78-0.83] in tumor cases, outperforming previous state-of-the-art method. Acceptability testing resulted in an 89% acceptance rate after adjudication by a tie-breaker(N=108 MRIs), and TissUnet demonstrated excellent performance in the blinded comparative review (N=45 MRIs), including both healthy and tumor cases in pediatric populations. TissUnet enables fast, accurate, and reproducible segmentation of extracranial tissues, supporting large-scale studies on craniofacial morphology, treatment effects, and cardiometabolic risk using standard brain T1w MRI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05660 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TissUnet: Improved Extracranial Tissue and Cranium Segmentation for Children through Adulthood Mandzak, Markiian Yang, Elvira Zapaishchykova, Anna Chen, Yu-Hui Heilbroner, Lucas Zielke, John Tak, Divyanshu Mojahed-Yazdi, Reza Mussa, Francesca Romana Ye, Zezhong Vajapeyam, Sridhar Benitez, Viviana Salloum, Ralph Chi, Susan N. Sotoudeh, Houman Seidlitz, Jakob Mueller, Sabine Aerts, Hugo J. W. L. Poussaint, Tina Y. Kann, Benjamin H. Computer Vision and Pattern Recognition Artificial Intelligence Extracranial tissues visible on brain magnetic resonance imaging (MRI) may hold significant value for characterizing health conditions and clinical decision-making, yet they are rarely quantified. Current tools have not been widely validated, particularly in settings of developing brains or underlying pathology. We present TissUnet, a deep learning model that segments skull bone, subcutaneous fat, and muscle from routine three-dimensional T1-weighted MRI, with or without contrast enhancement. The model was trained on 155 paired MRI-computed tomography (CT) scans and validated across nine datasets covering a wide age range and including individuals with brain tumors. In comparison to AI-CT-derived labels from 37 MRI-CT pairs, TissUnet achieved a median Dice coefficient of 0.79 [IQR: 0.77-0.81] in a healthy adult cohort. In a second validation using expert manual annotations, median Dice was 0.83 [IQR: 0.83-0.84] in healthy individuals and 0.81 [IQR: 0.78-0.83] in tumor cases, outperforming previous state-of-the-art method. Acceptability testing resulted in an 89% acceptance rate after adjudication by a tie-breaker(N=108 MRIs), and TissUnet demonstrated excellent performance in the blinded comparative review (N=45 MRIs), including both healthy and tumor cases in pediatric populations. TissUnet enables fast, accurate, and reproducible segmentation of extracranial tissues, supporting large-scale studies on craniofacial morphology, treatment effects, and cardiometabolic risk using standard brain T1w MRI. |
| title | TissUnet: Improved Extracranial Tissue and Cranium Segmentation for Children through Adulthood |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.05660 |