Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT

Jun Xu; Rongguo Zhang; Zijian Zhou; Chunxue Wu; Qiang Gong; Huiling Zhang; Shuang Wu; Gang Wu; Yufeng Deng; Chen Xia; Jun Ma

doi:10.3389/fnins.2020.541817

Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT

Front Neurosci. 2021 Jan 11:14:541817. doi: 10.3389/fnins.2020.541817. eCollection 2020.

Authors

Jun Xu¹, Rongguo Zhang², Zijian Zhou², Chunxue Wu³, Qiang Gong², Huiling Zhang², Shuang Wu², Gang Wu¹, Yufeng Deng², Chen Xia², Jun Ma³

Affiliations

¹ Department of Radiology, QingPu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, China.
² Beijing Infervision Technology Co., Ltd., Beijing, China.
³ Department of Neuroradiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.

Abstract

Background: The ABC/2 method is usually applied to evaluate intracerebral hemorrhage (ICH) volume on computed tomography (CT), although it might be inaccurate and not applicable in estimating extradural or subdural hemorrhage (EDH, SDH) volume due to their irregular hematoma shapes. This study aimed to evaluate deep framework optimized for the segmentation and quantification of ICH, EDH, and SDH.

Methods: The training datasets were 3,000 images retrospectively collected from a collaborating hospital (Hospital A) and segmented by the Dense U-Net framework. Three experienced radiologists determined the ground truth by marking the pixels as hemorrhage area. We utilized the Dice and intra-class correlation coefficients (ICC) to test the reliability of the ground truth. Moreover, the testing datasets consisted of 211 images (internal test) from Hospital A, and 86 ICH images (external test) from another hospital (Hospital B). In this study, we chose scatter plots, ICC, and Pearson correlation coefficients (PCC) with ground truth to evaluate the performance of the deep framework. Furthermore, to validate the effectiveness of the deep framework, we did a comparative analysis of the hemorrhage volume estimation between the deep model and the ABC/2 method.

Results: The high Dice (0.89-0.95) and ICC (0.985-0.997) showed the consistency of the manual segmentations among the radiologists and the reliability of the ground truth. For the internal test, the Dice coefficients of ICH, EDH, and SDH were 0.90 ± 0.06, 0.88 ± 0.12, and 0.82 ± 0.16, respectively. For the external test, the segmentation Dice was 0.86 ± 0.09. Comparatively, the ICC and PCC of ICH volume estimations were 0.99 performed by Dense U-Net that overmatched the ABC/2 method.

Conclusion: This study revealed the excellent performance of hematoma segmentation and volume evaluation based on Dense U-Net, which indicated our deep framework might contribute to efficiently developing treatment strategies for intracranial hemorrhage in clinics.

Keywords: CT; deep learning; intracranial hemorrhage; quantification; segmentation.