Computed tomography image segmentation of irregular cerebral hemorrhage lesions based on improved U-Net

Objective: This paper aims to improve U-Net for more accurate segmentation of irregular intracranial hemorrhage lesions in CT images. Methods: The residual octave convolution (ResOctConv) module was introduced to overcome the semantic gap issue in U-Net, and a hybrid attention mechanism called mixed attention mechanism (MAM) was proposed to further enhance the performance of U-Net. 40 patients with irregular cerebral hemorrhage images were selected from head CT scans performed between August and December 2022. Two radiologists independently traced the edge of each selected image three times, and the final segmentation boundary was determined by consensus. The effectiveness of the lesion segmentation was measured using the Dice coefficient, Jaccard Index, and Relative volume difference. Results: Based on the box plot of the Dice coefficient for all 40 patients, the improved U-Net demonstrated higher accuracy in segmenting irregular intracranial hemorrhage lesions in CT images compared to the original U-Net. Moreover, the comparison of Dice coefficient, Jaccard coefficient, and RVD indicates that the improved U-Net outperforms both the original U-Net and the region growing algorithm in segmenting irregular cerebral hemorrhage lesions. Conclusions: The proposed improved U-Net outperforms both the original U-Net and the region growing algorithm in segmenting irregular cerebral hemorrhage lesions, providing an advanced toolset for radiologists to accurately identify and diagnose irregular cerebral hemorrhage lesions