Article
Computer Science, Artificial Intelligence
Lihao Liu, Angelica I. Aviles-Rivero, Carola-Bibiane Schonlieb
Summary: Medical image segmentation is a crucial task in medical imaging, and this paper presents a novel unsupervised segmentation technique called CLMorph. By combining registration and contrastive learning, the proposed technique achieves improved accuracy in image segmentation.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Automation & Control Systems
Nagaraj Yamanakkanavar, Bumshik Lee
Summary: In this paper, a multipath feature fusion convolutional neural network (MF2-Net) with novel and efficient spatial group convolution (SGC) modules is proposed for automated segmentation of medical images. Experimental results demonstrate that the proposed model improves the segmentation accuracy with fewer learnable parameters.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2022)
Article
Chemistry, Multidisciplinary
S. V. Aruna Kumar, Ehsan Yaghoubi, Hugo Proenca
Summary: Brain tissue segmentation is crucial for clinical diagnosis of brain diseases using multi-modal MRI. Existing unsupervised methods, such as K-Means, Expectation-Maximization, and Fuzzy Clustering, have limitations in handling complex and uncertain brain images. To address these issues, this research proposes a fuzzy consensus clustering algorithm that utilizes a voting schema to cluster pixels, demonstrating superior performance compared to state-of-the-art approaches. The proposed method is also proven effective in Autism Spectrum Disorder detection.
APPLIED SCIENCES-BASEL
(2022)
Article
Computer Science, Information Systems
Jiaofen Nan, Junya Su, Jincan Zhang
Summary: This paper proposes a technique of human brain image registration based on tissue morphology in vivo, which aims to address the problems of previous image registration. Different feature points, including those at the boundary of different brain tissues and those of the maximum or minimum from the original image, are extracted and combined. The correct matching pairs of feature points are used to generate the model parameters of spatial transformation, and the brain image registration is completed by combining interpolation techniques. The proposed method outperforms other algorithms in terms of quantitative indicators and spatial location, size, appearance contour, and registration details.
Review
Computer Science, Artificial Intelligence
Tommaso Ciceri, Letizia Squarcina, Alice Giubergia, Alessandra Bertoldo, Paolo Brambilla, Denis Peruzzo
Summary: Brain segmentation is crucial for quantitative analysis of the brain for clinical applications like fetal imaging. Various factors make fetal brain segmentation in MRI challenging, including fetal movements, rapid development, and limited imaging data. Deep learning techniques, especially convolutional neural networks, have become the state-of-the-art approach in this field due to their ability to provide reliable segmentation results across diverse datasets.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2023)
Article
Neurosciences
Mandong Hu, Yi Zhong, Shuxuan Xie, Haibin Lv, Zhihan Lv
Summary: The study improved the fuzzy clustering algorithm and designed a brain image processing and brain disease diagnosis prediction model based on fuzzy clustering and HPU-Net. Experimental results show that the improved algorithm has more nodes, lower energy consumption, and greater stability compared to other models under the same conditions.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Biology
Celine Trebeau, Jacques Boutet de Monvel, Gizem Altay, Jean-Yves Tinevez, Raphael Etournay
Summary: Efficient tools for extracting 2D surfaces from 3D microscopy data are essential for studying the complex cellular choreography of epithelium morphogenesis. The Zellige software allows the extraction of multiple surfaces with varying inclination, contrast, and texture from a 3D image, providing a solution to the limitations of existing methods. The software has been demonstrated to perform well on synthetic images and various biological samples.
Article
Engineering, Electrical & Electronic
Jiafan Zhuang, Zilei Wang, Bingke Wang
Summary: Video semantic segmentation aims to generate accurate semantic maps for each frame in a video. This article proposes a distortion-aware feature correction method to improve video segmentation performance by correcting features on distorted regions.
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
(2021)
Article
Computer Science, Interdisciplinary Applications
Nan Wang, Shaohui Lin, Xiaoxiao Li, Ke Li, Yunhang Shen, Yue Gao, Lizhuang Ma
Summary: This research proposes an efficient Transformer-based UNet model for medical image segmentation. By introducing the Transformer module, the model can simultaneously learn global semantic information and local spatial-detailed features, and improve fine-grained details through a local multi-scale fusion block.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2023)
Article
Computer Science, Interdisciplinary Applications
Ran Gu, Guotai Wang, Tao Song, Rui Huang, Michael Aertsen, Jan Deprest, Sebastien Ourselin, Tom Vercauteren, Shaoting Zhang
Summary: The study presented a comprehensive attention-based CNN (CA-Net) for medical image segmentation, which significantly improved accuracy and explainability. The CA-Net achieved better segmentation results for skin lesions, placenta, and fetal brain compared to the U-Net model, while reducing the model size by approximately 15 times.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Interdisciplinary Applications
Xu Chen, Chunfeng Lian, Li Wang, Hannah Deng, Tianshu Kuang, Steve Fung, Jaime Gateno, Pew-Thian Yap, James J. Xia, Dinggang Shen
Summary: An anatomy-regularized representation learning approach is proposed for segmentation-oriented cross-modality image synthesis, showing superiority in comparison with state-of-the-art cross-modality medical image segmentation methods.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Computer Science, Hardware & Architecture
Xiwang Xie, Xipeng Pan, Weidong Zhang, Jubai An
Summary: This paper proposes a context hierarchical integrated network named CHI-Net for medical image segmentation. By combining dense dilated convolution and stacked residual pooling, it can accurately segment salient regions from medical images in a purely task-driven manner.
COMPUTERS & ELECTRICAL ENGINEERING
(2022)
Article
Computer Science, Information Systems
Sangwoo Lee, Yejin Lee, Geongyu Lee, Sangheum Hwang
Summary: Deep segmentation networks typically consist of an encoder and decoder for feature extraction and restoration to produce segmentation results. A supervised contrastive embedding approach is proposed to enhance feature maps using contrastive loss for improved segmentation performance. Empirical results demonstrate the effectiveness of this method in enhancing segmentation performance across various architectures.
Article
Computer Science, Artificial Intelligence
Yunyun Yang, Xiaoyan Hou, Huilin Ren
Summary: This paper proposes an improved active contour model that accurately segments and corrects inhomogeneous images with high robustness and accuracy, while saving time.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Interdisciplinary Applications
Dian Qin, Jia-Jun Bu, Zhe Liu, Xin Shen, Sheng Zhou, Jing-Jun Gu, Zhi-Hua Wang, Lei Wu, Hui-Fen Dai
Summary: Recent advancements in applying convolutional neural networks to medical image segmentation have led to a more precise prediction results, but existing methods often rely on high computational complexity and storage, which is not practical. To address this issue, a new efficient architecture is proposed by distilling knowledge from well-trained networks to train a lightweight network, resulting in improved segmentation capability while maintaining runtime efficiency.
IEEE TRANSACTIONS ON MEDICAL IMAGING
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Nicholas J. Tustison, Brian B. Avants, Zixuan Lin, Xue Feng, Nicholas Cullen, Jaime F. Mata, Lucia Flors, James C. Gee, Talissa A. Altes, John P. Mugler, Kun Qing
ACADEMIC RADIOLOGY
(2019)
Article
Neurosciences
Long Xie, Sandhitsu R. Das, Arun Pilania, Molly Daffner, Grace E. Stockbower, Sudipto Dolui, Paul A. Yushkevich, John A. Detre, David A. Wolk
Correction
Mathematical & Computational Biology
John Muschelli, Adrian Gherman, Jean-Philippe Fortin, Brian Avants, Brandon Whitcher, Jonathan D. Clayden, Brian S. Caffo, Ciprian M. Crainiceanu
Article
Materials Science, Multidisciplinary
A. H. Aly, E. K. Lai, N. Yushkevich, R. H. Stoffers, J. H. Gorman, A. T. Cheung, H. Gorman, R. C. Gorman, P. A. Yushkevich, A. M. Pouch
Summary: The study aims to accurately reconstruct cardiac valve morphology and motion through image analysis algorithms, providing personalized descriptions for cardiac patients and insights into disease pathophysiology. Results demonstrate that automated 4D image analysis allows for reliable modeling of mitral valve dynamics, facilitating research on pathological and normal valves.
EXPERIMENTAL MECHANICS
(2021)
Article
Critical Care Medicine
James R. Stone, Brian B. Avants, Nicholas J. Tustison, Eric M. Wassermann, Jessica Gill, Elena Polejaeva, Kristine C. Dell, Walter Carr, Angela M. Yarnell, Matthew L. LoPresti, Peter Walker, Meghan O'Brien, Natalie Domeisen, Alycia Quick, Claire M. Modica, John D. Hughes, Francis J. Haran, Carl Goforth, Stephen T. Ahlers
JOURNAL OF NEUROTRAUMA
(2020)
Article
Radiology, Nuclear Medicine & Medical Imaging
Carrie E. Zimmerman, Pulkit Khandelwal, Long Xie, Hyunyeol Lee, Hee Kwon Song, Paul A. Yushkevich, Arastoo Vossough, Scott P. Bartlett, Felix W. Wehrli
Summary: This study evaluated an automatic multi-atlas segmentation pipeline for cranial vault images, eliminating the need for manual intervention. The results showed good agreement between CT and automated MRI-based 3D cranial vault renderings, effectively eliminating the labor-intensive manual segmentation process.
ACADEMIC RADIOLOGY
(2022)
Article
Clinical Neurology
Paul A. Yushkevich, Monica Munoz Lopez, Maria Mercedes Iniguez de Onzono Martin, Ranjit Ittyerah, Sydney Lim, Sadhana Ravikumar, Madigan L. Bedard, Stephen Pickup, Weixia Liu, Jiancong Wang, Ling Yu Hung, Jade Lasserve, Nicolas Vergnet, Long Xie, Mengjin Dong, Salena Cui, Lauren McCollum, John L. Robinson, Theresa Schuck, Robin de Flores, Murray Grossman, M. Dylan Tisdall, Karthik Prabhakaran, Gabor Mizsei, Sandhitsu R. Das, Emilio Artacho-Perula, Mari'a Del Mar Arroyo Jimenez, Mari'a Pilar Marcos Raba, Francisco Javier Molina Romero, Sandra Cebada Sanchez, Jose Carlos Delgado Gonzalez, Carlos De la Rosa-Prieto, Marta Corcoles Parada, Edward B. Lee, John Q. Trojanowski, Daniel T. Ohm, Laura E. M. Wisse, David A. Wolk, David J. Irwin, Ricardo Insausti
Summary: This study utilized ex vivo MRI and dense serial histological imaging to construct three-dimensional quantitative maps of neurofibrillary tangle burden in the medial temporal lobe, revealing significant variation along different anatomical regions. The findings provide valuable insights into the distribution of this neurodegenerative pathology and may support the development and validation of neuroimaging biomarkers.
Article
Geriatrics & Gerontology
Laura Em Wisse, Long Xie, Sandhitsu R. Das, Robin de Flores, Oskar Hansson, Mohamad Habes, Jimit Doshi, Christos Davatzikos, Paul A. Yushkevich, David A. Wolk
Summary: The study found that CSF p-tau levels partially mediated the effect of age on hippocampal atrophy rates, while no significant associations were observed for WMHs with temporal lobe structural changes. These results suggest a potential role of tau pathology in age-related MTL structural changes.
NEUROBIOLOGY OF AGING
(2022)
Article
Biology
Danni Tu, Manu S. Goyal, Jordan D. Dworkin, Samuel Kampondeni, Lorenna Vidal, Eric Biondo-Savin, Sandeep Juvvadi, Prashant Raghavan, Jennifer Nicholas, Karen Chetcuti, Kelly Clark, Timothy Robert-Fitzgerald, Theodore D. Satterthwaite, Paul Yushkevich, Christos Davatzikos, Guray Erus, Nicholas J. Tustison, Douglas G. Postels, Terrie E. Taylor, Dylan S. Small, Russell T. Shinohara
Summary: A central challenge in medical imaging studies is to extract biomarkers that can characterize disease pathology or outcomes. This paper presents a fully automated framework for translating radiological diagnostic criteria into image-based biomarkers, with excellent classification performance.
Article
Clinical Neurology
Claire Andre, Elizabeth Kuhn, Stephane Rehel, Valentin Ourry, Solene Demeilliez-Servouin, Cassandre Palix, Francesca Felisatti, Pierre Champetier, Sophie Dautricourt, Paul Yushkevich, Denis Vivien, Vincent de la Sayette, Gael Chetelat, Robin de Flores, Geraldine Rauchs, Medit Ageing Res Grp
Summary: This study aimed to investigate the association between sleep disordered breathing (SDB) and medial temporal lobe neurodegeneration, as well as subsequent episodic memory impairment. The study found that SDB was associated with reduced volumes of medial temporal lobe subregions in amyloid-positive individuals, but not in amyloid-negative individuals. Additionally, lower baseline volumes of the whole hippocampus and CA1 were associated with worse episodic memory performance at follow-up.
Article
Neuroimaging
Alessandra M. Valcarcel, John Muschelli, Dzung L. Pham, Melissa Lynne Martin, Paul Yushkevich, Rachel Brandstadter, Kristina R. Patterson, Matthew K. Schindler, Peter A. Calabresi, Rohit Bakshi, Russell T. Shinohara
NEUROIMAGE-CLINICAL
(2020)
Meeting Abstract
Clinical Neurology
Lauren McCollum, Laura Wisse, Salena Cui, Robin de Flores, Sandhitsu Das, Long Xie, Paul Yushkevich, David Wolk
Meeting Abstract
Clinical Neurology
Lauren McCollum, Laura Wisse, Sandhitsu Das, Robin de Flores, Paul Yushkevich, David Wolk
Article
Mathematical & Computational Biology
John Muschelli, Adrian Gherman, Jean-Philippe Fortin, Brian Avants, Brandon Whitcher, Jonathan D. Clayden, Brian S. Caffo, Ciprian M. Crainiceanu
Article
Radiology, Nuclear Medicine & Medical Imaging
Ping Chiao, Barry J. Bedell, Brian Avants, Alex P. Zijdenbos, Marilyn Grand'Maison, Paul O'Neill, John O'Gorman, Tianle Chen, Robert Koeppe
JOURNAL OF NUCLEAR MEDICINE
(2019)
Article
Neurosciences
Jose Sanchez-Bornot, Roberto C. Sotero, J. A. Scott Kelso, Ozguer Simsek, Damien Coyle
Summary: This study proposes a multi-penalized state-space model for analyzing unobserved dynamics, using a data-driven regularization method. Novel algorithms are developed to solve the model, and a cross-validation method is introduced to evaluate regularization parameters. The effectiveness of this method is validated through simulations and real data analysis, enabling a more accurate exploration of cognitive brain functions.