改进型Unet网络在脑CT图像出血区域识别与分割中的应用

周正松; 陈旭淼; 张皞宇; 万红丽; 赵杰祎; 张韬; 王晓宇

doi:10.12182/20220160302

改进型Unet网络在脑CT图像出血区域识别与分割中的应用

Application of Improved Unet Network in the Recognition and Segmentation of Hemorrhage Regions in Brain CT Images

摘要

摘要:
目的探讨改进型Unet网络技术对脑CT图像出血区域识别与分割的性能及其应用价值。
方法回顾性纳入476份自发性脑出血（spontaneous intracerebral hemorrhage, SICH）患者的脑CT图像，采用基于改进型Unet网络对患者脑CT图像出血区域进行识别和分割，由临床医生手动标注出血区域的影像数据，通过随机排序后，选取来自106例患者的430份数据进入训练集，11例患者的46份数据进入测试集，实验数据集通过数据增强后，经过网络训练和模型测试，以检验分割性能，并将分割结果与Unet网络（Base）、FCN-8s和Unet++网络进行对比。
结果改进型Unet网络对脑CT图像出血区域的分割中，相似性系数（Dice）、正向预测系数（PPV）、灵敏度系数（SC）三项评价指标分别达到0.8738、0.9011和0.8648，相较于FCN-8s网络分别提升8.80%、7.14%和8.96%，相较于Unet网络（Base）分别提升4.56%、4.44%和4.15%，同时优于Unet++网络的分割性能。
结论本文提出的基于Unet网络的改进方法对脑CT出血区域的识别和分割具有良好的性能，是一种脑CT出血区域识别和分割的适用方法，对于辅助临床决策和预防早期血肿扩张具有潜在应用价值。

Abstract:
Objective To examine the performance and application value of improved Unet network technology in the recognition and segmentation of hemorrhage regions in brain CT images.
Methods A total of 476 brain CT images of patients with spontaneous intracerebral hemorrhage (SICH) were retrospectively included. The improved Unet network was used to identify and segment the hemorrhage regions in the patients’ brain CT images. The CT imaging data of the hemorrhage regions were manually labelled by clinicians. After randomized sorting, 430 data sets from 106 patients were selected for inclusion in the training set and 46 data sets from 11 patients were included in the test set. After data enhancement, the experimental data set underwent network training and model testing in order to assess the segmentation performance. The segmentation results were compared with the those of the Unet network (Base), FCN-8s network and Unet++ network.
Results In the segmentation of brain CT image hemorrhage region with the improved Unet network, the three evaluation indicators of Dice similarity coefficient, positive predictive value (PPV), and sensitivity coefficient (SC) reached 0.8738, 0.9011 and 0.8648, respectively, increasing by 8.80%, 7.14% and 8.96%, respectively, compared with those of FCN-8s, and increasing by 4.56%, 4.44% and 4.15%, respectively, compared with those of Unet network (Base). The improved Unet network also showed better segmentation performance than that of Unet++ network.
Conclusion The improved method based on Unet network proposed in this report displayed good performance in the recognition and segmentation of hemorrhage regions in brain CT images, and is an appropriate method for the recognition and segmentation of hemorrhage regions in brain CT images, showing potential application value for assisting clinical decision-making and preventing early hematoma expansion.

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