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Identifying risk factors of intracerebral hemorrhage stability using explainable attention model

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Abstract

Segmentation of intracerebral hemorrhage (ICH) helps improve the quality of diagnosis, draft the desired treatment methods, and clinically observe the variations with healthy patients. The clinical utilization of various ICH progression scoring systems has limitations due to the systems’ modest predictive value. This paper proposes a single pipeline of a multi-task model for end-to-end hemorrhage segmentation and risk estimation. We introduce a 3D spatial attention unit and integrate it into the state-of-the-art segmentation architecture, UNet, to enhance the accuracy by bootstrapping the global spatial representation. We further extract the geometric features from the segmented hemorrhage volume and fuse them with clinical features such as CT angiography (CTA) spot, Glasgow Coma Scale (GCS), and age to predict the ICH stability. Several state-of-the-art machine learning techniques such as multilayer perceptron (MLP), support vector machine (SVM), gradient boosting, and random forests are applied to train stability estimation and to compare the performances. To align clinical intuition with model learning, we determine the shapely values (SHAP) and explain the most significant features for the ICH risk scoring system. A total of 79 patients are included, of which 20 are found in critical condition. Our proposed single pipeline model achieves a segmentation accuracy of 86.3%, stability prediction accuracy of 78.3%, and precision of 82.9%; the mean square error of exact expansion rate regression is observed to be 0.46. The SHAP analysis reveals that CTA spot sign, age, solidity, location, and length of the first axis of the ICH volume are the most critical characteristics that help define the stability of the stroke lesion. We also show that integrating significant geometric features with clinical features can improve the ICH progression scoring by predicting long-term outcomes.

Overview of our proposed method comprising of spatial attention and feature extraction mechanisms. The architecture is trained on the input CT images, and the first step output is the predicted segmentation of the hemorrhagic region. The output is fed into a geometric feature extractor and is fused with clinical features to estimate ICH stability using a multilayer perceptron (MLP)

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Funding

This work is supported by the Singapore Academic Research Fund under Grant R397000353114 and the Shun Hing Institute of Advanced Engineering (SHIAE project BME-p1-21, 8115064) at the Chinese University of Hong Kong (CUHK).

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Authors and Affiliations

  1. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Seshasayi Rangaraj, Mobarakol Islam, Vibashan VS, Navodini Wijethilake, Utkarsh Uppal & Hongliang Ren

  2. NUS Graduate School for Integrative Sciences and Engineering (NGS), National University of Singapore, Singapore, Singapore

    Mobarakol Islam

  3. Department of ECE, National Institute of Technology, Tiruchirappalli, India

    Seshasayi Rangaraj & Vibashan VS

  4. Department of ENTC, University of Moratuwa, Moratuwa, Sri Lanka

    Navodini Wijethilake

  5. Department of Electrical Engineering, Punjab Engineering College, Chandigarh, India

    Utkarsh Uppal

  6. Department of Neurosurgery, National Neuroscience Institute, Singapore, Singapore

    Angela An Qi See, Jasmine Chan & Nicolas Kon Kam King

  7. Department of Anesthesiology & Neurology, Duke University, Durham, USA

    Michael Lucas James

  8. Neuro Asia Care, Mount Elizabeth Hospital, Singapore, Singapore

    Nicolas Kon Kam King

  9. Department of Electronic Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong (CUHK), Hong Kong, Hong Kong

    Hongliang Ren

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  1. Seshasayi Rangaraj
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Correspondence to Hongliang Ren.

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Rangaraj, S., Islam, M., VS, V. et al. Identifying risk factors of intracerebral hemorrhage stability using explainable attention model. Med Biol Eng Comput 60, 337–348 (2022). https://doi.org/10.1007/s11517-021-02459-y

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