A 2D U-NET combined model based on lesion size for automated stroke lesion segmentation

Ipsa Yadav; Vaibhav Bahel; François De Guio; Juhi Desai; Nikhil Gupta; Latha Poonamallee

doi:10.1117/12.2655136

7 April 2023 A 2D U-NET combined model based on lesion size for automated stroke lesion segmentation

Ipsa Yadav, Vaibhav Bahel, François De Guio, Juhi Desai, Nikhil Gupta, Latha Poonamallee

Author Affiliations +

Proceedings Volume 12465, Medical Imaging 2023: Computer-Aided Diagnosis; 1246535 (2023) https://doi.org/10.1117/12.2655136
Event: SPIE Medical Imaging, 2023, San Diego, California, United States

Conference Poster

Abstract

Worldwide, about 4-5 million deaths (9 percent) occur due to stroke. Stroke is the leading cause for neurological dysfunction resulting in considerable morbidity and mortality and holds the 6th position in the list of causes for reduced DALYS (Disability Adjusted Life Years). It’s categorized into two types: ischemic and hemorrhagic. Till date manual segmentation remains the gold standard in segmenting the stroke region of interest for every scan. This is a time-consuming process and inefficient in accurately identifying the ROIs. This work explores the hypothesis to develop an accurate automated stroke segmentation model. The 2D U-NET architecture used in segmenting the ROIs is based on the format to include the lesion size, lesion volume and their locations in the left and right hemisphere of the brain, and cortical and subcortical brain regions. The cohort consisted of single and multiple ROIs and were further bifurcated based on the volumes of these lesions. Based on this information we optimized our network into 5 different models keeping in mind all the information about the lesion size and volume. Eventually the average dice score obtained was highest for the network that included the combination of smaller and larger lesion, and Gd-T1w MRI scans containing the stroke lesion but no information of its size. The dice score for this model was 0.822 on the testing set. This result shows that considering the lesion sizes and volumes can help in obtaining a precise and automated segmentation model and can be the base work to further localize and classify the lesions.

Citation Download Citation

Ipsa Yadav, Vaibhav Bahel, François De Guio, Juhi Desai, Nikhil Gupta, and Latha Poonamallee "A 2D U-NET combined model based on lesion size for automated stroke lesion segmentation", Proc. SPIE 12465, Medical Imaging 2023: Computer-Aided Diagnosis, 1246535 (7 April 2023); https://doi.org/10.1117/12.2655136

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