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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. 

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2. 3DGAUnet: 3D Generative Adversarial Networks with a 3D U-Net Based Generator to Achieve the Accurate and Effective Synthesis of Clinical Tumor Image Data for Pancreatic Cancer

   https://doi.org/10.3390/cancers15235496  

   Shi, Yu ; Tang, Hannah ; Baine, Michael J. ; Hollingsworth, Michael A. ; Du, Huijing ; Zheng, Dandan ; Zhang, Chi ; Yu, Hongfeng ( December 2023 , Cancers)

   Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D PDAC, where the contrast is especially poor, owing to the high heterogeneity in both tumor and background tissues. In this study, we developed a new GAN-based model, named 3DGAUnet, for generating realistic 3D CT images of PDAC tumors and pancreatic tissue, which can generate the inter-slice connection data that the existing 2D CT image synthesis models lack. The transition to 3D models allowed the preservation of contextual information from adjacent slices, improving efficiency and accuracy, especially for the poor-contrast challenging case of PDAC. PDAC’s challenging characteristics, such as an iso-attenuating or hypodense appearance and lack of well-defined margins, make tumor shape and texture learning challenging. To overcome these challenges and improve the performance of 3D GAN models, our innovation was to develop a 3D U-Net architecture for the generator, to improve shape and texture learning for PDAC tumors and pancreatic tissue. Thorough examination and validation across many datasets were conducted on the developed 3D GAN model, to ascertain the efficacy and applicability of the model in clinical contexts. Our approach offers a promising path for tackling the urgent requirement for creative and synergistic methods to combat PDAC. The development of this GAN-based model has the potential to alleviate data scarcity issues, elevate the quality of synthesized data, and thereby facilitate the progression of deep learning models, to enhance the accuracy and early detection of PDAC tumors, which could profoundly impact patient outcomes. Furthermore, the model has the potential to be adapted to other types of solid tumors, hence making significant contributions to the field of medical imaging in terms of image processing models.

    

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3. 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. 

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4. 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. 

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5. HUMUS-Net: Hybrid Unrolled Multi-Scale Network Architecture for Accelerated MRI Reconstruction

   Fabian, Zalan ; Tinaz, Berk ; Soltanolkotabi, Mahdi ( January 2022 , Advances in neural information processing systems)

   In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of under-sampled and noisy measurements. Deep learning approaches have been proven to be successful in solving this ill-posed inverse problem and are capable of producing very high quality reconstructions. However, current architectures heavily rely on convolutions, that are content-independent and have difficulties modeling long-range dependencies in images. Recently, Transformers, the workhorse of contemporary natural language processing, have emerged as powerful building blocks for a multitude of vision tasks. These models split input images into nonoverlapping patches, embed the patches into lower-dimensional tokens and utilize a self-attention mechanism that does not suffer from the aforementioned weaknesses of convolutional architectures. However, Transformers incur extremely high compute and memory cost when 1) the input image resolution is high and 2) when the image needs to be split into a large number of patches to preserve fine detail information, both of which are typical in low-level vision problems such as MRI reconstruction, having a compounding effect. To tackle these challenges, we propose HUMUS-Net, a hybrid architecture that combines the beneficial implicit bias and efficiency of convolutions with the power of Transformer blocks in an unrolled and multi-scale network. HUMUS-Net extracts high-resolution features via convolutional blocks and refines low-resolution features via a novel Transformer-based multi-scale feature extractor. Features from both levels are then synthesized into a high-resolution output reconstruction. Our network establishes new state of the art on the largest publicly available MRI dataset, the fastMRI dataset. We further demonstrate the performance of HUMUS-Net on two other popular MRI datasets and perform fine-grained ablation studies to validate our design. 

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