融合SeNet与ResNet的编码器-解码器模型提升胶质瘤分割精度
Leveraging SeNet and ResNet Synergy within an Encoder-Decoder Architecture for Glioma Detection
- 用SeResNet-152作主干网络，结合编码器-解码器结构增强特征提取
- 在测试集上达87%的Dice系数、89.12%准确率和88%的IoU
- 适合医学影像分析人员参考，尤其关注脑肿瘤自动分割
脑肿瘤是严重危害患者健康的异常，可能导致癌症等危及生命的情况，并引发神经功能障碍、认知损伤、运动感觉缺陷以及情绪行为改变，显著影响生活质量，因此早期诊断与及时治疗至关重要。然而，从医学影像（特别是MRI）中精确分割肿瘤区域是一项耗时且复杂的任务，需依赖放射科医生的专业判断，手动分割还易出错。为此，本研究将SeNet与ResNet架构的协同效应融入编码器-解码器框架，专门用于胶质瘤检测与分割。所提模型采用SeResNet-152作为主干网络，嵌入稳健的编码器-解码器结构，以提升特征提取能力并改善分割精度。评估结果显示，该模型表现优异：Dice系数达87%，准确率为89.12%，交并比（IoU）为88%，平均交并比（mean IoU）为82%，充分证明其在复杂脑肿瘤分割任务中的有效性。
原文摘要 · Abstract (English)
Brain tumors are abnormalities that can severely impact a patient's health, leading to life-threatening conditions such as cancer. These can result in various debilitating effects, including neurological issues, cognitive impairment, motor and sensory deficits, as well as emotional and behavioral changes. These symptoms significantly affect a patient's quality of life, making early diagnosis and timely treatment essential to prevent further deterioration. However, accurately segmenting the tumor region from medical images, particularly MRI scans, is a challenging and time-consuming task that requires the expertise of radiologists. Manual segmentation can also be prone to human errors. To address these challenges, this research leverages the synergy of SeNet and ResNet architectures within an encoder-decoder framework, designed specifically for glioma detection and segmentation. The proposed model incorporates the power of SeResNet-152 as the backbone, integrated into a robust encoder-decoder structure to enhance feature extraction and improve segmentation accuracy. This novel approach significantly reduces the dependency on manual tasks and improves the precision of tumor identification. Evaluation of the model demonstrates strong performance, achieving 87% in Dice Coefficient, 89.12% in accuracy, 88% in IoU score, and 82% in mean IoU score, showcasing its effectiveness in tackling the complex problem of brain tumor segmentation.
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