用多精度训练提升脑肿瘤3D分割精度，关键指标达0.90
Enhanced 3D Brain Tumor Segmentation Using Assorted Precision Training
- 采用多精度训练策略优化SegResNet模型
- 全肿瘤分割Dice达0.90，核心区域0.84
- 适合医学影像分析与早期诊断研究者
脑肿瘤是影响各年龄段人群的严重疾病，表现为脑内非必要细胞的扩散，常见症状包括头痛、癫痫和感觉异常。本研究聚焦良性和恶性脑肿瘤，强调早期识别对患者生存的重要性。提出一种先进的3D脑肿瘤分割方法，基于广泛使用的SegResNet架构，结合自动多精度训练策略进行优化。采用Dice损失函数和评估指标，实验结果显示：整体肿瘤分割的Dice分数为0.90，肿瘤核心区域为0.84，增强肿瘤部分为0.79。该方法显著提升了分割精度，有助于实现早期精准诊断。
原文摘要 · Abstract (English)
A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of non-essential cells close to or throughout the brain. Symptoms of this ailment include headaches, seizures, and sensory changes. This research explores two main categories of brain tumors: benign and malignant. Benign spreads steadily, and malignant expresses growth, making it dangerous. Early identification of brain tumors is a crucial factor for the survival of patients. This research provides a state-of-the-art approach to the early identification of tumors within the brain. We implemented the SegResNet architecture, a widely adopted architecture for three-dimensional segmentation, and trained it using the automatic multi-precision method. We incorporated the dice loss function and dice metric for evaluating the model. We got a dice score of 0.84. For the tumor core, we got a dice score of 0.84; for the whole tumor, 0.90; and for the enhanced tumor, we got a score of 0.79.
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