构建199例肌骨软组织肿瘤MRI数据集，助力自动分割模型开发。
MSTT-199: MRI Dataset for Musculoskeletal Soft Tissue Tumor Segmentation
- 收集199例患者肌骨软组织肿瘤MRI数据，标注完整。
- 模型零微调即达0.79的Dice分数，性能领先。
- 发现纤维性和血管性肿瘤分割困难，因异质性强。
准确分割肌骨软组织肿瘤对评估肿瘤大小、位置、诊断及治疗反应至关重要，直接影响患者预后。然而，手动分割依赖临床经验，耗时费力。自动化分割模型需大规模标注数据集支持。本文构建了包含199例患者的肌骨软组织肿瘤MRI数据集。基于该数据集训练的分割模型在公开数据集上未微调即达到0.79的Dice分数，表明数据集的多样性和实用性。分析显示，模型在纤维性和血管性肿瘤上的表现较差，原因在于其解剖位置多变、尺寸差异大及信号强度异质性显著。代码与模型已开源于https://github.com/Reasat/mstt。
原文摘要 · Abstract (English)
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
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