Neural network for autonomous segmentation and volumetric assessment of clot and edema in acute and subacute intracerebral hemorrhages

Thomas Lilieholm; Alan McMillan; Azam Ahmed; Matthew Henningsen; Matthew Larson; Walter F Block

doi:10.1016/j.mri.2023.07.015

Neural network for autonomous segmentation and volumetric assessment of clot and edema in acute and subacute intracerebral hemorrhages

Magn Reson Imaging. 2023 Nov:103:162-168. doi: 10.1016/j.mri.2023.07.015. Epub 2023 Aug 2.

Authors

Thomas Lilieholm¹, Alan McMillan², Azam Ahmed³, Matthew Henningsen⁴, Matthew Larson⁵, Walter F Block²

Affiliations

¹ Department of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA. Electronic address: tlilieholm@wisc.edu.
² Department of Medical Physics, University of Wisconsin at Madison, Madison, WI, USA; Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA; Deparment of Biomedical Engineering, University of Wisconsin at Madison, Madison, WI, USA.
³ Department of Neurosurgery, University of Wisconsin at Madison, Madison, WI, USA.
⁴ Department of Electrical Engineering, University of Wisconsin at Madison, Madison, WI, USA.
⁵ Department of Radiology, University of Wisconsin at Madison, Madison, WI, USA.

PMID: 37541456
PMCID: PMC10528387 (available on 2024-11-01)
DOI: 10.1016/j.mri.2023.07.015

Abstract

Introduction: Minimally-invasive surgical techniques for intracerebral hemorrhage (ICH) evacuation use imaging to guide the suction, lysing and/or drainage from the hemorrhage site via various designs. A previous international surgical study has shown that reduction of hematoma volume below 15 ml is indicative of improved long term patient outcomes. The study noted a need for tools to periodically visualize remaining clot during intervention to increase the likelihood of evacuating sufficient clot volumes without endangering rebleeds. Robust segmentation of MRI could guide surgeons and radiologists regarding remaining regions and approaches for prudent evacuation. We thus propose a Convolutional Neural Network (CNN) to identify and autonomously segment clot and peripheral edema in MR images of the brain and generate an estimate of the remaining clot volume.

Materials and methods: We used a retrospective, locally-acquired dataset of ICH patient scans taken on 3 T MRI scanners. Three sets of ground truth manual segmentations were independently generated by two imaging scientists and one radiology fellow. Evaluation of clot age was determined based on relative contrast of hemorrhage components and reviewed by a neurosurgeon. Model accuracy was determined by pixel-wise Dice coefficient (DC) calculations between each ground truth manual segmentation and the machine-derived autonomous segmentations.

Results: The model produced autonomous segmentations of clot core with an average DC of 0.75 ± 0.21 relative to manual segmentations of the same scans. For edema, it produced segmentations with an average DC of 0.68 ± 0.16 relative to manual. From these pixel-wise segmentations, clot volume can be calculated. Model-produced segmentations underestimated clot volumes by an average of 17% relative to ground-truth.

Conclusion: The machine learning models were able to identify and segment volumes of ICH components swiftly and accurately.

Keywords: Image segmentation; Interventional tools; Intracerebral hemorrhage; Machine learning.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Brain
Cerebral Hemorrhage* / diagnostic imaging
Edema
Humans
Image Processing, Computer-Assisted / methods
Magnetic Resonance Imaging / methods
Neural Networks, Computer*
Retrospective Studies

Abstract

Publication types

MeSH terms

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