Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network

Ariel Iporre-Rivas; Dorothee Saur; Karl Rohr; Gerik Scheuermann; Christina Gillmann

doi:10.1117/1.JMI.10.4.044502

17 July 2023 Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network

Ariel Iporre-Rivas, Dorothee Saur, Karl Rohr, Gerik Scheuermann, Christina Gillmann

Author Affiliations +

Journal of Medical Imaging, Vol. 10, Issue 4, 044502 (July 2023). https://doi.org/10.1117/1.JMI.10.4.044502

Abstract

Purpose

The interpretation of image data plays a critical role during acute brain stroke diagnosis, and promptly defining the requirement of a surgical intervention will drastically impact the patient’s outcome. However, determining stroke lesions purely from images can be a daunting task. Many studies proposed automatic segmentation methods for brain stroke lesions from medical images in different modalities, though heretofore results do not satisfy the requirements to be clinically reliable. We investigate the segmentation of brain stroke lesions using a geometric deep learning model that takes advantage of the intrinsic interconnected diffusion features in a set of multi-modal inputs consisting of computer tomography (CT) perfusion parameters.

Approach

We propose a geometric deep learning model for the segmentation of ischemic stroke brain lesions that employs spline convolutions and unpooling/pooling operators on graphs to excerpt graph-structured features in a fully convolutional network architecture. In addition, we seek to understand the underlying principles governing the different components of our model. Accordingly, we structure the experiments in two parts: an evaluation of different architecture hyperparameters and a comparison with state-of-the-art methods.

Results

The ablation study shows that deeper layers obtain a higher Dice coefficient score (DCS) of up to 0.3654. Comparing different pooling and unpooling methods shows that the best performing unpooling method is the proportional approach, yet it often smooths the segmentation border. Unpooling achieves segmentation results more adapted to the lesion boundary corroborated with systematic lower values of Hausdorff distance. The model performs at the level of state-of-the-art models without optimized training methods, such as augmentation or patches, with a DCS of 0.4553 ± 0.0031.

Conclusions

We proposed and evaluated an end-to-end trainable fully convolutional graph network architecture using spline convolutional layers for the ischemic stroke lesion prediction. We propose a model that employs graph-based operations to predict acute stroke brain lesions from CT perfusion parameters. Our results prove the feasibility of using geometric deep learning to solve segmentation problems, and our model shows a better performance than other models evaluated. The proposed model achieves improved metric values for the DCS metric, ranging from 8.61% to 69.05%, compared with other models trained under the same conditions. Next, we compare different pooling and unpooling operations in relation to their segmentation results, and we show that the model can produce segmentation outputs that adapt to irregular segmentation boundaries when using simple heuristic unpooling operations.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Ariel Iporre-Rivas, Dorothee Saur, Karl Rohr, Gerik Scheuermann, and Christina Gillmann "Stroke-GFCN: ischemic stroke lesion prediction with a fully convolutional graph network," Journal of Medical Imaging 10(4), 044502 (17 July 2023). https://doi.org/10.1117/1.JMI.10.4.044502

Received: 14 June 2022; Accepted: 20 June 2023; Published: 17 July 2023

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KEYWORDS

Education and training

Image segmentation

Ischemic stroke

Data modeling

Brain

Deep learning

Batch normalization

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