Dr. Timur Urakov Profile

Dr. Timur Urakov

at Univ of Miami

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Publications (2)

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Proceedings Article | 2 April 2024 Poster + Paper

A sequential geometry reconstruction based deep learning approach to improve accuracy and consistence of lumbar spine MRI image segmentation

Linchen Qian, Jiasong Chen, Linhai Ma, Timur Urakov, Liang Liang

Proceedings Volume 12926, 1292634 (2024) https://doi.org/10.1117/12.3007064

KEYWORDS: Image segmentation, Deformation, Spine, Magnetic resonance imaging, Feature extraction, Image processing, Data modeling, Statistical modeling, Image quality

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It is known that lumbar disc degeneration, a progressive structural wear and tear of lumbar intervertebral disc, is regarded as an essential factor on low back pain, a global health concern. Magnetic Resonance Imaging (MRI) plays a crucial role in detecting the morphologic changes and internal information of tissues. Thus, it is essential to develop accurate semantic segmentation of lumbar spine MRI images, which can assist doctors in recognizing the state of spinal degeneration in order to determine a suitable treatment. Recently, deep learning approaches have shown remarkable performance in medical image segmentation. However, the existing segmentation models often generate erroneously fragmented regions due to reasons related to texture similarity, image quality, or noises. In this work, we propose a novel neural network for the geometry reconstruction of lumbar spine from 2D mid-sagittal MRI images. We incorporated a UNet-style backbone with sequential geometry deformation modules to predict shape deformation on varying scales. We developed image feature extraction in a shape-specific manner to reject irrelevant information. We utilized self-attention mechanism to further process the extracted shape representation fused with image features and with a template as position embedding. We compared our model with some well-known models for image segmentation, including UNet++, Attention UNet, TransUNet, Swin-Unet, and UTNet. The results demonstrate that our model has the best performance, and its segmentation results are highly accurate and free of erroneous fragments. The source code is available at https://github.com/linchenq/SPIE2024-Geometry-Deformation-Lumbar-Spine-Segmentation.

Proceedings Article | 15 February 2021 Presentation + Paper

Adversarial robustness study of convolutional neural network for lumbar disk shape reconstruction from MR images

Jiasong Chen, Linchen Qian, Timur Urakov, Weiyong Gu, Liang Liang

Proceedings Volume 11596, 1159615 (2021) https://doi.org/10.1117/12.2580852

KEYWORDS: Performance modeling, Magnetic resonance imaging, Convolutional neural networks, Data modeling, Medical imaging, Image segmentation, Spine

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Machine learning technologies using deep neural networks (DNNs), especially convolutional neural networks (CNNs), have made automated, accurate, and fast medical image analysis a reality for many applications, and some DNN-based medical image analysis systems have even been FDA-cleared. Despite the progress, challenges remain to build DNNs as reliable as human expert doctors. It is known that DNN classifiers may not be robust to noises: by adding a small amount of noise to an input image, a DNN classifier may make a wrong classification of the noisy image (i.e., in-distribution adversarial sample), whereas it makes the right classification of the clean image. Another issue is caused by out-of-distribution samples that are not similar to any sample in the training set. Given such a sample as input, the output of a DNN will become meaningless. In this study, we investigated the in-distribution (IND) and out-of-distribution (OOD) adversarial robustness of a representative CNN for lumbar disk shape reconstruction from spine MR images. To study the relationship between dataset size and robustness to IND adversarial attacks, we used a data augmentation method to create training sets with different levels of shape variations. We utilized the PGD-based algorithm for IND adversarial attacks and extended it for OOD adversarial attacks to generate OOD adversarial samples for model testing. The results show that IND adversarial training can improve the CNN robustness to IND adversarial attacks, and larger training datasets may lead to higher IND robustness. However, it is still a challenge to defend against OOD adversarial attacks.

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