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.
KEYWORDS: Education and training, Object detection, Image segmentation, Adversarial training, Medical imaging, Magnetic resonance imaging, Data modeling, Data analysis, Biomedical applications, Statistical modeling
It is known that deep neural networks (DNNs) are vulnerable to adversarial noises. Improving adversarial robustness of DNNs is essential. This is not only because unperceivable adversarial noise is a threat to the performance of DNNs models, but also adversarially robust DNNs have a strong resistance to the white noises that may present everywhere in the actual world. To improve adversarial robustness of DNNs, a variety of adversarial training methods have been proposed. Most of the previous methods are designed under one single application scenario: image classification. However, image segmentation, landmark detection, and object detection are more commonly observed than classifying the entire images in the medical imaging field. Although classification tasks and other tasks (e.g., regression) share some similarities, they also differ in certain ways, e.g., some adversarial training methods use misclassification criteria, which is well-defined in classification but not in regression. These restrictions/limitations hinder application of adversarial training for many medical imaging analysis tasks. In our work, the contributions are as follows: (1) We investigated the existing adversarial training methods and discovered the challenges that make those methods unsuitable for adaptation in segmentation and detection tasks. (2) We modified and adapted some existing adversarial training methods for medical image segmentation and detection tasks. (3) We proposed a general adversarial training method for medical image segmentation and detection. (4) We implemented our method in diverse medical imaging tasks using publicly available datasets, including MRI segmentation, Cephalometric landmark detection, and blood cell detection. The experiments substantiated the effectiveness of our method.
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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