Publications by authors named "Soorena Salari"
Robust landmark-based brain shift correction with a Siamese neural network in ultrasound-guided brain tumor resection.
Int J Comput Assist Radiol Surg
March 2023
Purpose: In brain tumor surgery, tissue shift (called brain shift) can move the surgical target and invalidate the surgical plan. A cost-effective and flexible tool, intra-operative ultrasound (iUS) with robust image registration algorithms can effectively track brain shift to ensure surgical outcomes and safety.
Methods: We proposed to employ a Siamese neural network, which was first trained using natural images and fine-tuned with domain-specific data to automatically detect matching anatomical landmarks in iUS scans at different surgical stages.
Article Synopsis
- * A new deep learning model, which fuses features from both types of images, has been developed to achieve high accuracy in classifying large datasets, taking into account the uncertainty of predictions.
- * This model demonstrated impressive performance with 99.08% accuracy for CT scans and 96.35% for X-rays, and it is robust against noise and unfamiliar data; the code is publicly accessible.
Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset.
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