6 papers
Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application
Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov +6
We systematically evaluate a Deep Learning (DL) method in a 3D medical image segmentation task. Our segmentation method is integrated into the radiosurgery treatment process and di…
Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation
Boris Shirokikh, Alexey Shevtsov, Anvar Kurmukov +5
Target imbalance affects the performance of recent deep learning methods in many medical image segmentation tasks. It is a twofold problem: class imbalance - positive class (lesion…
Multi-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting
Artem Pimkin, Alexander Samoylenko, Natalia Antipina +4
Recent CT Metal Artifacts Reduction (MAR) methods are often based on image-to-image convolutional neural networks for adjustment of corrupted sinograms or images themselves. In thi…
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Boris Shirokikh, Alexandra Dalechina, Alexey Shevtsov +7
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors. As part of the therapy treatment, accurate delineation o…
Brain Tumor Image Retrieval via Multitask Learning
Maxim Pisov, Gleb Makarchuk, Valery Kostjuchenko +3
Classification-based image retrieval systems are built by training convolutional neural networks (CNNs) on a relevant classification problem and using the distance in the resulting…
Tumor Delineation For Brain Radiosurgery by a ConvNet and Non-Uniform Patch Generation
Egor Krivov, Valery Kostjuchenko, Alexandra Dalechina +5
Deep learning methods are actively used for brain lesion segmentation. One of the most popular models is DeepMedic, which was developed for segmentation of relatively large lesions…