Analysis of the Effects of Segmentation Networks and Loss Functions in Ischemic Stroke Lesion Segmentation

Year 2022, Issue: 40, 82 - 87, 30.09.2022

Ahmet Furkan Bayram , Alperen Derin , Caglar Gurkan , Abdulkadir Budak , Hakan Karataş

https://doi.org/10.31590/ejosat.1173070

Cited By: 2

Abstract

Stroke was the cause of one out of every six deaths from cerebrovascular disease in 2020. A stroke occurs in the United States (US) every 40 seconds. Every 3.5 minutes, people die of a stroke. More than total 795,000 stroke cases occur yearly in the US. This study aims to detect the ischemic stroke lesion that occurs in the brain. The Ischemic Stroke Lesion Segmentation (ISLES) 2017 data set, which includes 82 Magnetic Resonance images of 43 patients, was used. The UNet, Attention UNet, Residual UNet, Attention Residual UNet, and Residual UNet++ segmentation networks were tested. Moreover, Cross Entropy, Dice, IoU, Tversky, Focal Tversky, and their compound forms were analyzed. The IoU loss function tested on Attention UNet achieved the best performance with the dice score of 0.766, the IoU score of 0.621, the sensitivity of 0.730, the specificity of 0.997, the precision of 0.805, and the accuracy of 0.993.

Keywords

Stroke, Segmentation, Artificial intelligence, Machine learning, Deep learning

Thanks

This paper has been prepared by AKGUN Computer Incorporated Company. We would like to thank AKGUN Computer Inc. for providing all kinds of opportunities and funds for the execution of this project.

İskemik İnme Lezyon Segmentasyonunda Segmentasyon Ağlarının ve Kayıp Fonksiyonlarının Etkilerinin Analizi

Abstract

2020'de serebrovasküler hastalıklardan her altı ölümden birinin nedeni inmeydi. Amerika Birleşik Devletleri'nde (ABD) her 40 saniyede bir inme vakası görülmektedir. Her 3.5 dakikada bir insan inmeden hayatını kaybetmektedir. ABD'de yılda toplamda 795.000'den fazla inme vakası meydana gelmektedir. Bu çalışma, beyinde oluşan iskemik inme lezyonunu tespit etmeyi amaçlamaktadır. 43 hastanın 82 Manyetik Rezonans görüntüsünü içeren İskemik İnme Lezyon Segmentasyonu (ISLES) 2017 veri seti kullanıldı. UNet, Attention UNet, Residual UNet, Attention Residual UNet ve Residual UNet++ segmentasyon ağları test edilmiştir. Ayrıca Cross Entropy, Dice, IoU, Tversky, Focal Tversky ve bunların bileşik formları incelenmiştir. Attention UNet üzerinde test edilen IoU kayıp fonksiyonu 0.766 Zar skoru, 0.621 IoU skoru, 0.730 duyarlılık, 0.997 özgüllük, 0.805 kesinlik ve 0.993 doğruluk ile en iyi performansı elde etmiştir.

Keywords

İnme, Segmentasyon, Yapay zeka, Makine öğrenmesi, Derin öğrenme

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