Intensity variations in image texture can provide powerful quantitative information about physical properties of biological tissue. However, tissue patterns can vary according to the utilized imaging system and are intrinsically correlated to the scale of analysis. In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing image texture, but the subtle intra-heterogeneities within a given mass are difficult to capture via this model as it works at a single spatial scale. This paper proposes a locally adaptive 3D multi-resolution Nakagami-based fractal feature descriptor that extends Nakagami-based texture analysis to accommodate subtle speckle spatial frequency tissue intensity variability in volumetric scans. Local textural fractal descriptors - which are invariant to affine intensity changes - are extracted from volumetric patches at different spatial resolutions from voxel lattice-based generated shape and scale Nakagami parameters. Using ultrasound radio-frequency datasets we found that after applying an adaptive fractal decomposition label transfer approach on top of the generated Nakagami voxels, tissue characterization results were superior to the state of art. Experimental results on real 3D ultrasonic pre-clinical and clinical datasets suggest that describing tumor intra-heterogeneity via this descriptor may facilitate improved prediction of therapy response and disease characterization.
3D-Guided Scalable Flow Matching for Generating Volumetric Tissue Spatial Transcriptomics from Serial Histology
Cambridge Stem Cell Institute, University of Cambridge（剑桥干细胞研究所，剑桥大学）；Cambridge Center for AI in Medicine, University of Cambridge（剑桥人工智能医学中心，剑桥大学）；Department of Medicine, University of Cambridge（剑桥大学医学系）；Computer Science Department, University of Oxford（牛津大学计算机科学系）；Computer Science Department, University of Helsinki（赫尔辛基大学计算机科学系）；Institute for Molecular Medicine Finland, University of Helsinki（芬兰分子医学研究所，赫尔辛基大学）；Wellcome Sanger Institute（沃森桑格研究所）
VoxelSynth3D: Interpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC Benchmark
University of Massachusetts at Amherst（马萨诸塞大学阿默斯特分校）；New York University Abu Dhabi (NYUAD)（纽约大学阿布扎比分校）；Indian Institute of Technology Delhi Abu Dhabi（印度理工学院德里分校阿布扎比校区）；NYUAD Research Institute（纽约大学阿布扎比分校研究院）
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛与麻省理工学院布罗德研究所）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Harvard-MIT Division of Health Sciences and Technology（哈佛-麻省理工卫生科学与技术部）；Massachusetts Institute of Technology（麻省理工学院）；Pusan National University（釜山国立大学）；Emory University School of Medicine（埃默里大学医学院）；University of Cambridge（剑桥大学）；Rutgers Robert Wood Johnson Medical School（罗格斯大学罗伯特伍德约翰逊医学院）；Johns Hopkins Hospital（约翰霍普金斯医院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Institute of Fundamental Technological Research, Polish Academy of Sciences（波兰科学院基础技术研究所）
We propose a novel deep learning based approach to breast mass segmentation in ultrasound (US) imaging. In comparison to commonly applied segmentation methods, which use US images, our approach is based on quantitative entropy parametric maps. To segment the breast masses we utilized an attention gated U-Net convolutional neural network. US images and entropy maps were generated based on raw US signals collected from 269 breast masses. The segmentation networks were developed separately using US image and entropy maps, and evaluated on a test set of 81 breast masses. The attention U-Net traine…
University of Notre Dame（圣母大学）；University of Pittsburgh（匹兹堡大学）；Guangdong Provincial People’s Hospital（广东省人民医院）
The success of deep learning heavily depends on the availability of large labeled training sets. However, it is hard to get large labeled datasets in medical image domain because of the strict privacy concern and costly labeling efforts. Contrastive learning, an unsupervised learning technique, has been proved powerful in learning image-level representations from unlabeled data. The learned encoder can then be transferred or fine-tuned to improve the performance of downstream tasks with limited labels. A critical step in contrastive learning is the generation of contrastive data pairs, which i…
QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Guangdong Medical University（广东医科大学）
PHASOR: Anatomy- and Phase-Consistent Volumetric Diffusion for CT Virtual Contrast Enhancement
Shanghai Key Lab of Intelligent Information Processing, School of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院上海市智能信息处理重点实验室）；Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University（复旦大学类脑智能科学与技术研究院及教育部脑科学前沿中心）；Shanghai Center for Brain Science and Brain-inspired Technology（上海脑科学与类脑研究中心）；Department of Radiology, First Affiliated Hospital of College of Medical Science, Zhejiang University（浙江大学医学院附属第一医院放射科）；Alibaba DAMO Academy（阿里巴巴达摩院）
Erasmus MC（伊拉斯谟大学医学中心）；University of Copenhagen（哥本哈根大学）；COSMONiO；Hvidovre Hospital（维兹奥勒医院）；Bispebjerg Hospital（比斯佩比约医院）
We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading system, in which grades correspond to intervals (label example: 1-5% of diseased tissue). The proposed architecture encodes the knowledge that the labels represent a volumetric proportion. A custom loss is designed to learn with intervals. Thus, during training, our network learns to segment the diseased tissue such that its proportions fit the ground truth intervals. Our architecture and lo…
Automatic segmentation of brain MR images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is critical for tissue volumetric analysis and cortical surface reconstruction. Due to dramatic structural and appearance changes associated with developmental and aging processes, existing brain tissue segmentation methods are only viable for specific age groups. Consequently, methods developed for one age group may fail for another. In this paper, we make the first attempt to segment brain tissues across the entire human lifespan (0-100 years of age) using a unified deep learning…
Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting
University Hospital Basel（巴塞尔大学医院）；Helmholtz Center Munich（慕尼黑亥姆霍兹中心）；Helmholtz AI（亥姆霍兹人工智能）；Technical University of Munich（慕尼黑工业大学）；University of Basel（巴塞尔大学）；University of Zurich（苏黎世大学）；Klinikum rechts der Isar（伊斯河畔医院）；TranslaTUM - Central Institute for Translational Cancer Research（慕尼黑工业大学转化肿瘤学中央研究所（TranslaTUM））
Bilkent University（比尔肯特大学）；National Magnetic Resonance Research Center (UMRAM)（国家磁共振研究中心（UMRAM））；University of Southern California（南加州大学）；Hacettepe University（哈塞特佩大学）
Magnetic resonance imaging (MRI) offers the flexibility to image a given anatomic volume under a multitude of tissue contrasts. Yet, scan time considerations put stringent limits on the quality and diversity of MRI data. The gold-standard approach to alleviate this limitation is to recover high-quality images from data undersampled across various dimensions, most commonly the Fourier domain or contrast sets. A primary distinction among recovery methods is whether the anatomy is processed per volume or per cross-section. Volumetric models offer enhanced capture of global contextual information,…
University Medical Center Utrecht（乌得勒支大学医学中心）；Wilhelmina Children’s Hospital（威廉明娜儿童医院）；Utrecht University（乌得勒支大学）
MR images of fetuses allow clinicians to detect brain abnormalities in an early stage of development. The cornerstone of volumetric and morphologic analysis in fetal MRI is segmentation of the fetal brain into different tissue classes. Manual segmentation is cumbersome and time consuming, hence automatic segmentation could substantially simplify the procedure. However, automatic brain tissue segmentation in these scans is challenging owing to artifacts including intensity inhomogeneity, caused in particular by spontaneous fetal movements during the scan. Unlike methods that estimate the bias f…
Comparison of different segmentation algorithms on brain volume and fractal dimension in infant brain MRIs
Laboratory for Motion Analysis, Devision of Paediatric Orthopaedic, Children's Hospital of Eastern Switzerland（东部瑞士儿童医院运动分析实验室）；Lucerne University of Applied Sciences and Arts（卢塞恩应用科学与艺术大学）；TOELT llc, Machine Learning Research and Development（TOELT公司，机器学习研究与开发）；University of Ljubljana, Faculty of Computer and Information Science（卢布尔雅那大学，计算机与信息科学学院）
Henry M. Jackson Foundation（亨利·M·杰克逊基金会）；Johns Hopkins University（约翰斯·霍普金斯大学）；National Institutes of Health（美国国立卫生研究院）
Deep learning approaches to the segmentation of magnetic resonance images have shown significant promise in automating the quantitative analysis of brain images. However, a continuing challenge has been its sensitivity to the variability of acquisition protocols. Attempting to segment images that have different contrast properties from those within the training data generally leads to significantly reduced performance. Furthermore, heterogeneous data sets cannot be easily evaluated because the quantitative variation due to acquisition differences often dwarfs the variation due to the biologica…
The auditory ossicles that are located in the middle ear are the smallest bones in the human body. Their damage will result in hearing loss. It is therefore important to be able to automatically diagnose ossicles' diseases based on Computed Tomography (CT) 3D imaging. However CT images usually include the whole head area, which is much larger than the bones of interest, thus the localization of the ossicles, followed by segmentation, both play a significant role in automatic diagnosis. The commonly employed local segmentation methods require manually selected initial points, which is a highly …
SAGCNet: Spatial-Aware Graph Completion Network for Missing Slice Imputation in Population CMR Imaging
School of Engineering, College of Engineering and Physical Sciences（工程学院，工程与物理科学学院）；University of Birmingham（伯明翰大学）；Oxford Center for Clinical Magnetic Resonance Research (OCMR)（牛津临床磁共振研究中心）；University of Oxford（牛津大学）；John Radcliffe Hospital（约翰·拉德克利夫医院）；Johns Hopkins University School of Medicine（约翰霍普金斯大学医学院）；Queen Mary University London（伦敦女王大学）；Barts Health NHS Trust（巴特勒医院 NHS 信托）
Deep learning highly relies on the quantity of annotated data. However, the annotations for 3D volumetric medical data require experienced physicians to spend hours or even days for investigation. Self-supervised learning is a potential solution to get rid of the strong requirement of training data by deeply exploiting raw data information. In this paper, we propose a novel self-supervised learning framework for volumetric medical images. Specifically, we propose a context restoration task, i.e., Rubik's cube++, to pre-train 3D neural networks. Different from the existing context-restoration-b…
Automated Segmentation of Coronal Brain Tissue Slabs for 3D Neuropathology
Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州总医院和哈佛医学院生物医学成像中心）；Massachusetts Alzheimer’s Disease Research Center, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州阿尔茨海默病研究中心，马萨诸塞州总医院和哈佛医学院）；Department of Neurological Surgery, University of Washington School of Medicine（华盛顿大学医学院神经外科部门）；Department of Laboratory Medicine and Pathology, University of Washington School of Medicine（华盛顿大学医学院实验室医学与病理学部门）；Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology（麻省理工学院计算机科学与人工智能实验室）；Hawkes Institute, University College London（伦敦大学学院霍克斯研究所）
A Plug-and-Play Framework for Volumetric Light-Sheet Image Reconstruction
Department of Mathematics（数学系）；The University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Department of Bioengineering（生物工程系）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
A. K. Choudhury School of Information Technology, University of Calcutta（加尔各答大学A.K.乔杜里信息技术学院）；Indian Statistical Institute（印度统计研究所）
Magnetic Resonance Imaging (MRI) is a technology for non-invasive imaging of anatomical features in detail. It can help in functional analysis of organs of a specimen but it is very costly. In this work, methods for (i) virtual three-dimensional (3D) reconstruction from a single sequence of two-dimensional (2D) slices of MR images of a human spine and brain along a single axis, and (ii) generation of missing inter-slice data are proposed. Our approach helps in preserving the edges, shape, size, as well as the internal tissue structures of the object being captured. The sequence of original 2D …
Label- and slide-free tissue histology using 3D epi-mode quantitative phase imaging and virtual H&E staining
University of California, Davis（加利福尼亚大学戴维斯分校）；Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）；Emory University School of Medicine（埃默里大学医学院）；UC Davis Health（加州大学戴维斯分校医疗系统）
Simon Fraser University（西蒙弗雷泽大学）；Voronoi Health Analytics Incorporated（沃罗诺伊健康分析公司）
The latest advances in computer-assisted precision medicine are making it feasible to move from population-wide models that are useful to discover aggregate patterns that hold for group-based analysis to patient-specific models that can drive patient-specific decisions with regard to treatment choices, and predictions of outcomes of treatment. Body Composition is recognized as an important driver and risk factor for a wide variety of diseases, as well as a predictor of individual patient-specific clinical outcomes to treatment choices or surgical interventions. 3D CT images are routinely acqui…
CT-LungNet: A Deep Learning Framework for Precise Lung Tissue Segmentation in 3D Thoracic CT Scans
Machine Vision and Medical Image Processing (MVMIP) Laboratory, Faculty of Electrical and Computer Engineering, K.N. Toosi University of Technology（K.N.图西工业大学电气与计算机工程学院机器视觉与医学图像处理实验室）；Tehran University of Medical Sciences（德黑兰医科大学）；University of Delaware（特拉华大学）；École de technologie supérieure (ÉTS), Université du Québec（魁北克大学高等技术学院）；Centre de recherche de l’Institut universitaire de gériatrie de Montréal (CRIUGM)（蒙特利尔大学老年学研究所研究中心）；Department of Biomedical Engineering, K.N. Toosi University of Technology（K.N.图西工业大学生物医学工程系）
Ultrasound is the second most used modality in medical imaging. It is cost effective, hazardless, portable and implemented routinely in numerous clinical procedures. Nonetheless, image quality is characterized by granulated appearance, poor SNR and speckle noise. Specific for malignant tumors, the margins are blurred and indistinct. Thus, there is a great need for improving ultrasound image quality. We hypothesize that this can be achieved, using neural networks, by translation into a more realistic display which mimics an anatomical cut through the tissue. In order to achieve this goal, the p…
Leibniz Universität Hannover（汉诺威莱布尼茨大学）；Institute of Mechatronic Systems（机电系统研究所）
In microsurgery, lasers have emerged as precise tools for bone ablation. A challenge is automatic control of laser bone ablation with 4D optical coherence tomography (OCT). OCT as high resolution imaging modality provides volumetric images of tissue and foresees information of bone position and orientation (pose) as well as thickness. However, existing approaches for OCT based laser ablation control rely on external tracking systems or invasively ablated artificial landmarks for tracking the pose of the OCT probe relative to the tissue. This can be superseded by estimating the scene flow cause…
