MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor Segmentation
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929487239708672 |
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| author | Reasat, Tahsin Chenard, Stephen Rekulapelli, Akhil Chadwick, Nicholas Shechtel, Joanna van Schaik, Katherine Smith, David S. Lawrenz, Joshua |
| author_facet | Reasat, Tahsin Chenard, Stephen Rekulapelli, Akhil Chadwick, Nicholas Shechtel, Joanna van Schaik, Katherine Smith, David S. Lawrenz, Joshua |
| contents | Accurate musculoskeletal soft tissue tumor segmentation is vital for assessing tumor size, location, diagnosis, and response to treatment, thereby influencing patient outcomes. However, segmentation of these tumors requires clinical expertise, and an automated segmentation model would save valuable time for both clinician and patient. Training an automatic model requires a large dataset of annotated images. In this work, we describe the collection of an MR imaging dataset of 199 musculoskeletal soft tissue tumors from 199 patients. We trained segmentation models on this dataset and then benchmarked them on a publicly available dataset. Our model achieved the state-of-the-art dice score of 0.79 out of the box without any fine tuning, which shows the diversity and utility of our curated dataset. We analyzed the model predictions and found that its performance suffered on fibrous and vascular tumors due to their diverse anatomical location, size, and intensity heterogeneity. The code and models are available in the following github repository, https://github.com/Reasat/mstt |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03110 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor Segmentation Reasat, Tahsin Chenard, Stephen Rekulapelli, Akhil Chadwick, Nicholas Shechtel, Joanna van Schaik, Katherine Smith, David S. Lawrenz, Joshua Image and Video Processing Computer Vision and Pattern Recognition Accurate musculoskeletal soft tissue tumor segmentation is vital for assessing tumor size, location, diagnosis, and response to treatment, thereby influencing patient outcomes. However, segmentation of these tumors requires clinical expertise, and an automated segmentation model would save valuable time for both clinician and patient. Training an automatic model requires a large dataset of annotated images. In this work, we describe the collection of an MR imaging dataset of 199 musculoskeletal soft tissue tumors from 199 patients. We trained segmentation models on this dataset and then benchmarked them on a publicly available dataset. Our model achieved the state-of-the-art dice score of 0.79 out of the box without any fine tuning, which shows the diversity and utility of our curated dataset. We analyzed the model predictions and found that its performance suffered on fibrous and vascular tumors due to their diverse anatomical location, size, and intensity heterogeneity. The code and models are available in the following github repository, https://github.com/Reasat/mstt |
| title | MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor Segmentation |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.03110 |