Abstract
Ischemic stroke lesion segmentation from MRI (Magnetic Resonance Imaging) is the process of separating normal and ischemic stroke pixels. In clinical routine, remains a difficult problem, as ischemic stroke lesions have a complicated structure in shape. This paper aims to present a new approach for segmentation of brain ischemic stroke lesions from temperature distribution. Ischemic stroke is the consequence of the lack of blood flow and metabolic heat generation; therefore, the temperature distribution in the ischemic area is changed compared to healthy tissues. In this paper, ischemic stroke lesion segmentation is carried out using U-Net neural network based on temperature changes in the lesion zone. The temperature distribution in the brain with the ischemic stroke was calculated using the Pennes bioheat transfer equation and then transformed to grayscale thermal images with additional Gaussian noise. Then, U-Net was used for ischemic stroke segmentation from the generated thermal images. A dataset containing 440 thermal images was generated to train the U-Net architecture. NVIDIA Geforce GTX 1060 6 GB GPU was used to speed up the training process of U-Net. The network was tested in 19 thermal images, and yields a precise segmentation with F1 score = 0.758, Accuracy = 0.969, Precision = 0.7102, and Recall = 0.8836. The training time was 5 h and 1 min. The obtained results will be used to reinforce segmentation from MRI for more accurate diagnosis.
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Bousselham, A., Bouattane, O., Youssfi, M., Raihani, A. (2020). Ischemic Stroke Lesion Segmentation Based on Thermal Analysis Model Using U-Net Fully Convolutional Neural Networks on GPUs. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1106. Springer, Cham. https://doi.org/10.1007/978-3-030-36677-3_12
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