Journal of Digital Imaging

, Volume 32, Issue 3, pp 450–461 | Cite as

Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models

  • Junghwan ChoEmail author
  • Ki-Su Park
  • Manohar Karki
  • Eunmi Lee
  • Seokhwan Ko
  • Jong Kun Kim
  • Dongeun Lee
  • Jaeyoung Choe
  • Jeongwoo Son
  • Myungsoo Kim
  • Sukhee Lee
  • Jeongho Lee
  • Changhyo Yoon
  • Sinyoul Park


Highly accurate detection of the intracranial hemorrhage without delay is a critical clinical issue for the diagnostic decision and treatment in an emergency room. In the context of a study on diagnostic accuracy, there is a tradeoff between sensitivity and specificity. In order to improve sensitivity while preserving specificity, we propose a cascade deep learning model constructed using two convolutional neural networks (CNNs) and dual fully convolutional networks (FCNs). The cascade CNN model is built for identifying bleeding; hereafter the dual FCN is to detect five different subtypes of intracranial hemorrhage and to delineate their lesions. Using a total of 135,974 CT images including 33,391 images labeled as bleeding, each of CNN/FCN models was trained separately on image data preprocessed by two different settings of window level/width. One is a default window (50/100[level/width]) and the other is a stroke window setting (40/40). By combining them, we obtained a better outcome on both binary classification and segmentation of hemorrhagic lesions compared to a single CNN and FCN model. In determining whether it is bleeding or not, there was around 1% improvement in sensitivity (97.91% [± 0.47]) while retaining specificity (98.76% [± 0.10]). For delineation of bleeding lesions, we obtained overall segmentation performance at 80.19% in precision and 82.15% in recall which is 3.44% improvement compared to using a single FCN model.


Cascaded deep learning model Lesion segmentation Sensitivity CT window setting Fully convolutional networks Intracranial hemorrhage 


Compliance with Ethical Standards

This data collection was reviewed and approved by the ethics committee at Kyungpook National University Hospital and Kyungpook National University Hospital Chilgok (KNUH 2017-06-005 and KNUCH 2016-11-050).


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Copyright information

© Society for Imaging Informatics in Medicine 2019

Authors and Affiliations

  • Junghwan Cho
    • 1
    Email author
  • Ki-Su Park
    • 2
  • Manohar Karki
    • 1
  • Eunmi Lee
    • 1
  • Seokhwan Ko
    • 1
  • Jong Kun Kim
    • 3
  • Dongeun Lee
    • 3
  • Jaeyoung Choe
    • 3
  • Jeongwoo Son
    • 3
  • Myungsoo Kim
    • 2
  • Sukhee Lee
    • 4
  • Jeongho Lee
    • 5
  • Changhyo Yoon
    • 6
  • Sinyoul Park
    • 7
  1. 1.CAIDE Systems Inc.LowellUSA
  2. 2.Department of NeurosurgerySchool of Medicine of Kyungpook National UniversityDaeguSouth Korea
  3. 3.Department of Emergency MedicineSchool of Medicine of Kyungpook National UniversityDaeguSouth Korea
  4. 4.Department of Emergency MedicineSchool of Medicine of Daegu Catholic UniversityDaeguSouth Korea
  5. 5.Department of NeurosurgeryDaegu Fatima HospitalDaeguSouth Korea
  6. 6.Department of NeurologyChangwon Hospital Gyeongsang National UniversityChangwonSouth Korea
  7. 7.Department of Emergency MedicineCollege of Medicine of Yeungnam UniversityDaeguSouth Korea

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