Research on Automatic Segmentation of CT Images of Intracerebral Hemorrhage Based on
Deep Learning Segmentation Model
MIAO Zheng1, LI Mingyang1, CHEN Zhongping1, WANG Shuo1, WANG Zhuo1, ZHANG Lei1, CHEN Lizhou2,
CHEN Yuntian2, SHI Shengxian1, LI Hao1, SHI Guang1, ZHU Wanan1
1. Department of Radiology, The First Hospital of Jilin University, Changchun Jilin 130000, China; 2. Department of Radiology,
West China Hospital Sichuan University, Chengdu Sichuan 610041, China
Abstract：Objective To achieve automatic segmentation of hematoma region in CT image of patients with intracerebral hemorrhage
based on artificial intelligence deep learning method, and evaluate the optimization effect of post-processing algorithm on
segmentation results. Methods The imaging data of patients with intracerebral hemorrhage in the First Hospital of Jilin University
from April 2018 to August 2020 were retrospectively analyzed. 416 patients were included in the study according to the inclusion
criteria. They were randomly divided into 291 cases in the training set and 125 cases in the test set according to the ratio of 7∶3.
After the CT images of patients were preprocessed, straightening and bone removal, an automatic segmentation network ADUNET
proposed in this study was used for training to realize the automatic segmentation of hematoma area. Finally, the post-processing
algorithm was used to further optimize the segmentation results, and the Dice coefficient, Hausdorff-Distance (HD) coefficient and
other evaluation indicators were compared and analyzed. Results Compared with other two mainstream segmentation networks,
ADUNET proposed in this design achieved the best segmentation results on this data set (average Dice was 0.895, average HD
was 11.62), and verifies that the post-processing algorithm could further optimize the segmentation results and improve the
segmentation accuracy (average Dice was 0.899, average HD was 11.33). Conclusion The ADUNET segmentation network
and post-processing algorithm proposed in this study can realize the automatic segmentation and optimization of intracerebral
hemorrhage area based on CT image. This method can improve the diagnosis efficiency and optimize the diagnosis process, and
has high clinical application value.
苗政1，李明洋1，陈忠萍1，王烁1，王卓1，张磊1，陈丽舟2，陈云天2，史晟先1，李昊1，石光1，朱万安1. 基于深度学习分割模型的脑出血CT图像自动分割研究[J]. 中国医疗设备, 2022, 37(8): 46-50.
MIAO Zheng1, LI Mingyang1, CHEN Zhongping1, WANG Shuo1, WANG Zhuo1, ZHANG Lei1, CHEN Lizhou2,
CHEN Yuntian2, SHI Shengxian1, LI Hao1, SHI Guang1, ZHU Wanan1. Research on Automatic Segmentation of CT Images of Intracerebral Hemorrhage Based on
Deep Learning Segmentation Model. China Medical Devices, 2022, 37(8): 46-50.
