More Like this

1. BNU-Net: A Novel Deep Learning Approach for LV MRI Analysis in Short-Axis MRI

   https://doi.org/10.1109/BIBE.2019.00137  

   Chu, Wenhui; Molina, Giovanni; Navkar, Nikhil V.; Eick, Christoph F.; Becker, Aaron T.; Tsiamyrtzis, Panagiotis; Tsekos, Nikolaos V. (October 2019, 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE))

   This work presents a novel deep learning architecture called BNU-Net for the purpose of cardiac segmentation based on short-axis MRI images. Its name is derived from the Batch Normalized (BN) U-Net architecture for medical image segmentation. New generations of deep neural networks (NN) are called convolutional NN (CNN). CNNs like U-Net have been widely used for image classification tasks. CNNs are supervised training models which are trained to learn hierarchies of features automatically and robustly perform classification. Our architecture consists of an encoding path for feature extraction and a decoding path that enables precise localization. We compare this approach with a parallel approach named U-Net. Both BNU-Net and U-Net are cardiac segmentation approaches: while BNU-Net employs batch normalization to the results of each convolutional layer and applies an exponential linear unit (ELU) approach that operates as activation function, U-Net does not apply batch normalization and is based on Rectified Linear Units (ReLU). The presented work (i) facilitates various image preprocessing techniques, which includes affine transformations and elastic deformations, and (ii) segments the preprocessed images using the new deep learning architecture. We evaluate our approach on a dataset containing 805 MRI images from 45 patients. The experimental results reveal that our approach accomplishes comparable or better performance than other state-of-the-art approaches in terms of the Dice coefficient and the average perpendicular distance. 

   more » « less
2. Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI

   https://doi.org/10.3390/diagnostics12010123  

   Almajalid, Rania; Zhang, Ming; Shan, Juan (January 2022, Diagnostics)

   In the medical sector, three-dimensional (3D) images are commonly used like computed tomography (CT) and magnetic resonance imaging (MRI). The 3D MRI is a non-invasive method of studying the soft-tissue structures in a knee joint for osteoarthritis studies. It can greatly improve the accuracy of segmenting structures such as cartilage, bone marrow lesion, and meniscus by identifying the bone structure first. U-net is a convolutional neural network that was originally designed to segment the biological images with limited training data. The input of the original U-net is a single 2D image and the output is a binary 2D image. In this study, we modified the U-net model to identify the knee bone structures using 3D MRI, which is a sequence of 2D slices. A fully automatic model has been proposed to detect and segment knee bones. The proposed model was trained, tested, and validated using 99 knee MRI cases where each case consists of 160 2D slices for a single knee scan. To evaluate the model’s performance, the similarity, dice coefficient (DICE), and area error metrics were calculated. Separate models were trained using different knee bone components including tibia, femur, patella, as well as a combined model for segmenting all the knee bones. Using the whole MRI sequence (160 slices), the method was able to detect the beginning and ending bone slices first, and then segment the bone structures for all the slices in between. On the testing set, the detection model accomplished 98.79% accuracy and the segmentation model achieved DICE 96.94% and similarity 93.98%. The proposed method outperforms several state-of-the-art methods, i.e., it outperforms U-net by 3.68%, SegNet by 14.45%, and FCN-8 by 2.34%, in terms of DICE score using the same dataset. 

   more » « less
3. MULTI-COMPONENT DEFORMABLE MODELS COUPLED WITH 2D-3D U-NET FOR AUTOMATED PROBABILISTIC SEGMENTATION OF CARDIAC WALLS AND BLOOD

   Yang, D.; Huang, Q.; Axel, L.; Metaxas, D. (April 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018))

   The segmentation of the ventricular wall and the blood pool in cardiac magnetic resonance imaging (MRI) has been inves- tigated for decades, given its important role for delineation of cardiac functioning and diagnosis of heart diseases. One of the major challenges is that the inner epicardium boundary is not always visible in the image domain, due to the mix- ture of blood and muscle structures, especially at the end of contraction, or systole. To address it, we propose a novel ap- proach for the cardiac segmentation in the short-axis (SAX) MRI: coupled deep neural networks and deformable models. First, a 2D U-Net is adopted for each magnetic resonance (MR) slice, and a 3D U-Net refines the segmentation results along the temporal dimension. Then, we propose a multi- component deformable model to extract accurate contours for both endo- and epicardium with global and local constraints. Finally, a partial blood classification is explored to estimate the presence of boundary pixels near the trabeculae and solid wall, and to avoid moving the endocardium boundary inward. Quantitative evaluation demonstrates the high accuracy, ro- bustness, and efficiency of our approach for the slices ac- quired at different locations and different cardiac phases. 

   more » « less
4. FMG-Net and W-Net: Multigrid Inspired Deep Learning Architectures for Medical Imaging Segmentation

   Celaya, Adrian; Riviere, Beatrice; Fuentes, David (December 2023, https://research.latinxinai.org/workshops/neurips/neurips-2023.html)

   Accurate medical imaging segmentation is critical for precise and effective medi- cal interventions. However, despite the success of convolutional neural networks (CNNs) in medical image segmentation, they still face challenges in handling fine-scale features and variations in image scales. These challenges are particularly evident in complex and challenging segmentation tasks, such as the BraTS multi- label brain tumor segmentation challenge. In this task, accurately segmenting the various tumor sub-components, which vary significantly in size and shape, remains a significant challenge, with even state-of-the-art methods producing substantial errors. Therefore, we propose two architectures, FMG-Net and W-Net, that incor- porate the principles of geometric multigrid methods for solving linear systems of equations into CNNs to address these challenges. Our experiments on the BraTS 2020 dataset demonstrate that both FMG-Net and W-Net outperform the widely used U-Net architecture regarding tumor subcomponent segmentation accuracy and training efficiency. These findings highlight the potential of incorporating the principles of multigrid methods into CNNs to improve the accuracy and efficiency of medical imaging segmentation. 

   more » « less
5. Ex-Vivo Hippocampus Segmentation Using Diffusion-Weighted MRI

   https://doi.org/10.3390/math12070940  

   Tang , Haoteng; Dai, Siyuan; Zou, Eric M; Liu, Guodong; Ahearn, Ryan; Krafty, Ryan; Modo, Michel; Zhan, Liang (April 2024, Mathematics)

   The hippocampus is a crucial brain structure involved in memory formation, spatial navigation, emotional regulation, and learning. An accurate MRI image segmentation of the human hippocampus plays an important role in multiple neuro-imaging research and clinical practice, such as diagnosing neurological diseases and guiding surgical interventions. While most hippocampus segmentation studies focus on using T1-weighted or T2-weighted MRI scans, we explore the use of diffusion-weighted MRI (dMRI), which offers unique insights into the microstructural properties of the hippocampus. Particularly, we utilize various anisotropy measures derived from diffusion MRI (dMRI), including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, for a multi-contrast deep learning approach to hippocampus segmentation. To exploit the unique benefits offered by various contrasts in dMRI images for accurate hippocampus segmentation, we introduce an innovative multimodal deep learning architecture integrating cross-attention mechanisms. Our proposed framework comprises a multi-head encoder designed to transform each contrast of dMRI images into distinct latent spaces, generating separate image feature maps. Subsequently, we employ a gated cross-attention unit following the encoder, which facilitates the creation of attention maps between every pair of image contrasts. These attention maps serve to enrich the feature maps, thereby enhancing their effectiveness for the segmentation task. In the final stage, a decoder is employed to produce segmentation predictions utilizing the attention-enhanced feature maps. The experimental outcomes demonstrate the efficacy of our framework in hippocampus segmentation and highlight the benefits of using multi-contrast images over single-contrast images in diffusion MRI image segmentation. 

   more » « less