Modern optical coherence tomography (OCT) devices provide volumetric images with micrometer-scale spatial resolution and a temporal resolution beyond video rate. In this work, we analyze an OCT-based prototypical tracking system which processes 831 volumes per second, estimates translational motion, and automatically adjusts the field-of-view, which has a size of few millimeters, to follow a sample even along larger distances. The adjustment is realized by two galvo mirrors and a motorized reference arm, such that no mechanical movement of the scanning setup is necessary. Without requiring a marker or any other knowledge about the sample, we demonstrate that reliable tracking of velocities up to 25 mm/s is possible with mean tracking errors in the order of 0.25 mm. Further, we report successful tracking of lateral velocities up to 70 mm/s with errors below 0.3 mm.
Clinical tracking systems are popular but typically require specific tracking markers. During the last years, scanning speed of optical coherence tomography (OCT) has increased to A-scan rates above 1 MHz allowing to acquire volume scans of moving objects. Thorefore, we propose a markerless tracking system based on OCT to obtain small volumetric images including information of sub-surface structures at high spatio-temporal resolution. In contrast to conventional vision based approaches, this allows identifying natural landmarks even for smooth and homogeneous surfaces. We describe the optomech…
Massachusetts Institute of Technology（麻省理工学院）；University of Oxford（牛津大学）
Imaging through dense scattering media - such as biological tissue, fog, and smoke - has applications in the medical and robotics fields. We propose a new framework using automatic differentiation for All Photons Imaging through homogeneous scattering media with unknown optical properties for non-invasive sensing and diagnostics. We overcome the need for the imaging target to be visible to the illumination source in All Photons Imaging, enabling practical and non-invasive imaging through turbid media with a simple optical setup. Our method does not require calibration to acquire the sensor pos…
Retinal optical coherence tomography (OCT) and OCT angiography (OCTA) suffer from the degeneration of image quality due to speckle noise and bulk-motion noise, respectively. Because the cross-sectional retina has distinct features in OCT and OCTA B-scans, existing digital filters that can denoise OCT efficiently are unable to handle the bulk-motion noise in OCTA. In this Letter, we propose a universal digital filtering approach that is capable of minimizing both types of noise. Considering the retinal capillaries in OCTA are hard to differentiate in B-scans while having distinct curvilinear st…
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））
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…
City College New York（纽约市立学院）；Memorial Sloan Kettering Cancer Center（纪念斯隆凯特琳癌症中心）
Purpose: Conventional automated segmentation of the head anatomy in MRI distinguishes different brain and non-brain tissues based on image intensities and prior tissue probability maps (TPM). This works well for normal head anatomies, but fails in the presence of unexpected lesions. Deep convolutional neural networks leverage instead spatial patterns and can learn to segment lesions, but often ignore prior probabilities. Approach: We add three sources of prior information to a three-dimensional convolutional network, namely, spatial priors with a TPM, morphological priors with conditional rand…
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…
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…
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）
Serial section electron microscopy (ssEM) is a widely used technique for obtaining volumetric information of biological tissues at nanometer scale. However, accurate 3D reconstructions of identified cellular structures and volumetric quantifications require precise estimates of section thickness and anisotropy (or stretching) along the XY imaging plane. In fact, many image processing algorithms simply assume isotropy within the imaging plane. To ameliorate this problem, we present a method for estimating thickness and stretching of electron microscopy sections using non-parametric Bayesian reg…
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）
A method for supervoxel-wise association studies of age and other non-imaging variables from coronary computed tomography angiograms
Radiology, Department of Surgical Sciences, Uppsala University（乌普萨拉大学放射科）；Antaros Medical（Antaros医疗）；Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg（哥德堡大学医学院分子与临床医学系）
Estimating the forces acting between instruments and tissue is a challenging problem for robot-assisted minimally-invasive surgery. Recently, numerous vision-based methods have been proposed to replace electro-mechanical approaches. Moreover, optical coherence tomography (OCT) and deep learning have been used for estimating forces based on deformation observed in volumetric image data. The method demonstrated the advantage of deep learning with 3D volumetric data over 2D depth images for force estimation. In this work, we extend the problem of deep learning-based force estimation to 4D spatio-…
Institute of Medical Technology, Hamburg University of Technology（汉堡工业大学医学技术研究所）
Forecasting motion of a specific target object is a common problem for surgical interventions, e.g. for localization of a target region, guidance for surgical interventions, or motion compensation. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution. Recently, deep learning methods have shown promising performance for OCT-based motion estimation based on two volumetric images. We extend this approach and investigate whether using a time series of volumes enables motion forecasting. We propose 4D spatio-temporal deep learning for end-to-end moti…
Weakly Supervised AI for Efficient Analysis of 3D Pathology Samples
Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）；Broad Institute of Harvard and MIT（哈佛与麻省理工学院博德研究所）；Dana-Farber Cancer Institute（丹娜法伯癌症研究院）；Harvard-MIT Division of Health Sciences and Technology（哈佛-麻省理工学院健康科学与技术分部）；Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）；Johns Hopkins Hospital（约翰霍普金斯医院）；The Ohio State University（俄亥俄州立大学）
MR-STAT is an emerging quantitative magnetic resonance imaging technique which aims at obtaining multi-parametric tissue parameter maps from single short scans. It describes the relationship between the spatial-domain tissue parameters and the time-domain measured signal by using a comprehensive, volumetric forward model. The MR-STAT reconstruction solves a large-scale nonlinear problem, thus is very computationally challenging. In previous work, MR-STAT reconstruction using Cartesian readout data was accelerated by approximating the Hessian matrix with sparse, banded blocks, and can be done o…
ShanghaiTech University（上海科技大学）；Shanghai Engineering Research Center of Intelligent Vision and Imaging（上海智能视觉与成像工程技术研究中心）；Shanghai Jiao Tong University（上海交通大学）；Zhangjiang Laboratory（张江实验室）
For collecting high-quality high-resolution (HR) MR image, we propose a novel image reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR images and achieve an arbitrary up-sampling rate for HR image reconstruction. In this work, we suppose the desired HR image as an implicit continuous function of the 3D image spatial coordinate and the thick-slice LR images as several sparse discrete samplings of this function. Then the super-resolution (SR) task is to learn the continuous volumetric function from a limited observations using an fully-connected neural network…
In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappropriate data split can result in overlap between the training and testing sets, with a large portion of the literature overlooking this aspect. In this study, the effect of improper dataset splitting on model evaluation is demonstrated for three classification tasks using three OC…
