Automated segmentation of chronic stroke lesion using efficient U-Net architecture

• Autorzy: Shin Hyunkwang , Agyeman Rockson , Rafiq Muhammad , Chang Min Cheol , Choi Gyu Sang

Identyfikatory

DOI

10.1016/j.bbe.2022.01.002

• Języki publikacji: EN

Abstrakty

EN

Stroke is the most common neurological condition worldwide and causes various sequelae, such as motor impairment, cognitive deficit, and language problems. Typically, a radiologist segments the lesion directly. This is time-consuming and relies on the subjective perception of a specialist, so studies that automatically segment stroke lesions are very important in this regard. Although recent medical image segmentation studies have achieved great results using deep learning techniques, there are still many trainable parameters, and long-term dependence problems still exist. To solve these problems, we propose an efficient U-Net (eUNet) for segmenting chronic stroke lesions. The proposed e-UNet incorporates a depthwise convolution-based e-block designed to efficiently reduce the trainable parameters. A global-feature attention block (GA-block) improves segmentation performance by capturing global features between the encoder and decoder. The proposed e-UNet reduces the number of trainable parameters by 3.75 times compared to U-Net.We used the Anatomical Tracings of Lesions After Stroke (ATLAS) dataset to evaluate e-UNet. The lesions segmentation performance of the proposed e-UNet achieved 59.2%, 45.5%, 77.7%, 52.3%, and 52.3% in Dice, IoU, precision, and recall, respectively, for the test dataset (8694 2D images).

Słowa kluczowe

EN

stroke lesion segmentation; MRI; global feature attention block; deep learning

PL

segmentacja zmian udarowych; MRI; uczenie głębokie

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 1
• Strony: 285--294
• Opis fizyczny: Bibliogr. 51 poz., rys., tab.

Twórcy

autor

Shin Hyunkwang

• Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Gyeongbuk-do, Republic of Korea

autor

Agyeman Rockson

• Institute of Networked and Embedded Systems, University of Klagenfurt, Klagenfurt am Wörthersee, Austria

autor

Rafiq Muhammad

• Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Gyeongbuk-do, Republic of Korea

autor

Chang Min Cheol

• Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, Republic of Korea

autor

Choi Gyu Sang

• Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Gyeongbuk-do, Republic of Korea

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