Abstract
Ischemic stroke is a cerebrovascular disease caused by a blockage in blood vessels of the brain. The early detection of the stroke helps in preventing the penumbra from turning into the core. So, early detection is essential. But the variability of the stroke lesion in size, location, and appearance makes the automatic segmentation of the stroke lesion difficult. Computed Tomography Perfusion (CTP) is more suitable because of its wide availability and the less acquisition time as compared to Magnetic Resonance Imaging (MRI). CTP parameter maps include Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF), Mean Transit Time (MTT), and Time to Peak (Tmax). In this paper, we propose a deep learning model derived from U-Net that can process all the perfusion parameter maps parallelly at the same time independently. This architecture helps in avoiding the necessity of developing and training different models to process the perfusion maps independently. The significant modifications in the proposed model are i) incorporation of group convolutions to process the parameter maps separately and ii) introduced element-wise summation of feature maps instead of concatenation. Also, the class imbalance problem in medical datasets makes the segmentations more challenging. This is overcome by employing a loss that is a combination of cross entropy and soft dice loss. The model is trained from scratch. We performed a 5-fold cross-validation on the data. The proposed model achieves the highest 0.441 as the dice coefficient in one fold and the average dice score is 0.421. The experimentation is conducted on Ischemic Stroke Lesion Segmentation Challenge (ISLES) 2018 dataset.
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Raju, C.S.P., Kirupakaran, A.M., Neelapu, B.C., Laskar, R.H. (2023). Ischemic Stroke Lesion Segmentation in CT Perfusion Images Using U-Net with Group Convolutions. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1776. Springer, Cham. https://doi.org/10.1007/978-3-031-31407-0_21
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