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Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients
ConclusionThe results of this work show that the proposed image-to-image translation method is effective at preserving stroke lesions in unpaired modality translation, supporting its potential as a tool for stroke image analysis in real-life scenarios.
Source: International Journal of Computer Assisted Radiology and Surgery - January 6, 2023 Category: Intensive Care Source Type: research

Deep learning for collateral evaluation in ischemic stroke with imbalanced data
ConclusionAn automatic and robust collateral assessment method that mitigates the issues with the small imbalanced dataset was developed. Computer-aided evaluation of collaterals can help decision-making of ischemic stroke treatment strategy in clinical settings.
Source: International Journal of Computer Assisted Radiology and Surgery - January 12, 2023 Category: Intensive Care Source Type: research

Temporally downsampled cerebral CT perfusion image restoration using deep residual learning
ConclusionThe trained model can restore the temporally downsampled 15-pass CTP to 30 passes very well. According to the contrast test, sufficient information cannot be restored with, e.g., simple interpolation method and deep convolutional generative adversarial network, but can be restored with the proposed CNN model. This method can be an optional way to reduce radiation dose during CTP imaging.
Source: International Journal of Computer Assisted Radiology and Surgery - October 30, 2019 Category: Intensive Care Source Type: research

Deep learning-based simultaneous registration and unsupervised non-correspondence segmentation of medical images with pathologies
ConclusionNCR-Net, a CNN for simultaneous image registration and unsupervised non-correspondence segmentation, is presented. Experimental results show the network ’s ability to segment non-correspondence regions in an unsupervised manner and its robust registration performance even in the presence of large pathologies.
Source: International Journal of Computer Assisted Radiology and Surgery - March 3, 2022 Category: Intensive Care Source Type: research

An explainable machine learning method for assessing surgical skill in liposuction surgery
ConclusionOur results demonstrate the strong abilities of explainable machine learning methods in objective surgical skill assessment. We believe that the machine learning model based on interpretive methods proposed in this article can improve the evaluation and training of liposuction surgery and provide objective assessment and training guidance for other surgeries.
Source: International Journal of Computer Assisted Radiology and Surgery - November 12, 2022 Category: Intensive Care Source Type: research