基于精细化残差U-Net的新生儿局灶性脑白质损伤分割模型

Segmentation model of neonatal punctate white matter lesion based on refined deep residual U-Net

针对新生儿局灶性脑白质损伤的病灶区域小而样本差异大导致的检测与分割病灶较为困难的问题,提出一种精细化深度残差U-Net模型,以对病灶进行精细的语义分割。首先,把核磁共振(MRI)图像裁剪成较小的图像块;其次,利用残差U-Net提取出每个图像块不同层次的深度特征;然后,将特征进行融合并输出每个图像块的病灶分布概率图;最后,由全连接条件随机场对拼接后的概率图进行优化得到最终的分割结果。在某合作医院提供的数据集上的评估结果显示,在仅使用T1序列单模态数据的情况下,该模型在分割新生儿局灶性脑白质损伤时,病灶边缘的分割精度得到提高,且模型抗干扰能力较好。该模型的Dice相似性系数达到了62.51%,敏感度达到69.76%,特异性达到99.96%,修正的Hausdorff距离降低到33.67。

The tiny lesion area and the large difference between samples of neonatal punctate white matter lesion make it difficult to detect and segment the lesion. To solve the problem, a refined deep residual U-Net was proposed to realize the fine semantic segment of the lesion. Firstly, a Magnetic Resonance Imaging(MRI) image was cut into small patches. Secondly, the deep features of multiple layers of each image patch were extracted by the residual U-Net. Then, the features were fused and the probability map of the lesion distribution of each image patch was obtained. Finally, the probability map after splicing was optimized by the fully-connected condition random field to obtain the final segmentation results. The performance of the algorithm was evaluated on a dataset provided by a cooperative hospital. The results show that with only T1 order unimodal data used, the proposed model has the lesion’s edge segmented more precisely, and the anti-interference ability of the model is prominent. The model has the Dice similarity coefficient of 62.51%, the sensitivity of 69.76%, the specificity of 99.96%, and the modified Hausdorff distance reduced to 33.67.