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Medical Image Analysis

Andrew Lang, Aaron Carass, Bruno M Jedynak, Sharon D Solomon, Peter A Calabresi, Jerry L Prince
Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer...
October 12, 2017: Medical Image Analysis
Xiaomei Zhao, Yihong Wu, Guidong Song, Zhenye Li, Yazhuo Zhang, Yong Fan
Accurate and reliable brain tumor segmentation is a critical component in cancer diagnosis, treatment planning, and treatment outcome evaluation. Build upon successful deep learning techniques, a novel brain tumor segmentation method is developed by integrating fully convolutional neural networks (FCNNs) and Conditional Random Fields (CRFs) in a unified framework to obtain segmentation results with appearance and spatial consistency. We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) fine-tuning the FCNNs and the CRF-RNN using image slices...
October 5, 2017: Medical Image Analysis
Zhongyu Li, Xiaofan Zhang, Henning Müller, Shaoting Zhang
Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval...
October 2, 2017: Medical Image Analysis
Wufeng Xue, Gary Brahm, Sachin Pandey, Stephanie Leung, Shuo Li
Cardiac left ventricle (LV) quantification is among the most clinically important tasks for identification and diagnosis of cardiac disease. However, it is still a task of great challenge due to the high variability of cardiac structure across subjects and the complexity of temporal dynamics of cardiac sequences. Full quantification, i.e., to simultaneously quantify all LV indices including two areas (cavity and myocardium), six regional wall thicknesses (RWT), three LV dimensions, and one phase (Diastole or Systole), is even more challenging since the ambiguous correlations existing among these indices may impinge upon the convergence and generalization of the learning procedure...
September 28, 2017: Medical Image Analysis
Martin Urschler, Thomas Ebner, Darko Štern
In approaches for automatic localization of multiple anatomical landmarks, disambiguation of locally similar structures as obtained by locally accurate candidate generation is often performed by solely including high level knowledge about geometric landmark configuration. In our novel localization approach, we propose to combine both image appearance information and geometric landmark configuration into a unified random forest framework integrated into an optimization procedure that iteratively refines joint landmark predictions by using the coordinate descent algorithm...
September 21, 2017: Medical Image Analysis
Zhengwang Wu, Yaozong Gao, Feng Shi, Guangkai Ma, Valerie Jewells, Dinggang Shen
Hippocampal subfields play important roles in many brain activities. However, due to the small structural size, low signal contrast, and insufficient image resolution of 3T MR, automatic hippocampal subfields segmentation is less explored. In this paper, we propose an automatic learning-based hippocampal subfields segmentation method using 3T multi-modality MR images, including structural MRI (T1, T2) and resting state fMRI (rs-fMRI). The appearance features and relationship features are both extracted to capture the appearance patterns in structural MR images and also the connectivity patterns in rs-fMRI, respectively...
September 21, 2017: Medical Image Analysis
Rutger H J Fick, Alexandra Petiet, Mathieu Santin, Anne-Charlotte Philippe, Stephane Lehericy, Rachid Deriche, Demian Wassermann
Effective representation of the four-dimensional diffusion MRI signal - varying over three-dimensional q-space and diffusion time τ - is a sought-after and still unsolved challenge in diffusion MRI (dMRI). We propose a functional basis approach that is specifically designed to represent the dMRI signal in this qτ-space. Following recent terminology, we refer to our qτ-functional basis as "qτ-dMRI". qτ-dMRI can be seen as a time-dependent realization of q-space imaging by Paul Callaghan and colleagues. We use GraphNet regularization - imposing both signal smoothness and sparsity - to drastically reduce the number of diffusion-weighted images (DWIs) that is needed to represent the dMRI signal in the qτ-space...
September 14, 2017: Medical Image Analysis
Christoph von Tycowicz, Felix Ambellan, Anirban Mukhopadhyay, Stefan Zachow
We propose a novel Riemannian framework for statistical analysis of shapes that is able to account for the nonlinearity in shape variation. By adopting a physical perspective, we introduce a differential representation that puts the local geometric variability into focus. We model these differential coordinates as elements of a Lie group thereby endowing our shape space with a non-Euclidean structure. A key advantage of our framework is that statistics in a manifold shape space becomes numerically tractable improving performance by several orders of magnitude over state-of-the-art...
September 14, 2017: Medical Image Analysis
Chuyang Ye
Diffusion magnetic resonance imaging (dMRI) captures the anisotropic pattern of water displacement in the neuronal tissue and allows noninvasive investigation of the complex tissue microstructure. A number of biophysical models have been proposed to relate the tissue organization with the observed diffusion signals, so that the tissue microstructure can be inferred. The Neurite Orientation Dispersion and Density Imaging (NODDI) model has been a popular choice and has been widely used for many neuroscientific studies...
September 6, 2017: Medical Image Analysis
Oualid M Benkarim, Gemma Piella, Miguel Angel González Ballester, Gerard Sanroma
Quantitative neuroimaging analyses often rely on the accurate segmentation of anatomical brain structures. In contrast to manual segmentation, automatic methods offer reproducible outputs and provide scalability to study large databases. Among existing approaches, multi-atlas segmentation has recently shown to yield state-of-the-art performance in automatic segmentation of brain images. It consists in propagating the labelmaps from a set of atlases to the anatomy of a target image using image registration, and then fusing these multiple warped labelmaps into a consensus segmentation on the target image...
September 1, 2017: Medical Image Analysis
Priya Aggarwal, Anubha Gupta, Ajay Garg
Motivated by recent interest in identification of functional brain networks, we develop a new multivariate approach for functional brain network identification and name it as Multivariate Vector Regression-based Connectivity (MVRC). The proposed MVRC method regresses time series of all regions to those of other regions simultaneously and estimates pairwise association between two regions with consideration of influence of other regions and builds the adjacency matrix. Next, modularity method is applied on the adjacency matrix to detect communities or functional brain networks...
August 30, 2017: Medical Image Analysis
Xin Yang, Chaoyue Liu, Zhiwei Wang, Jun Yang, Hung Le Min, Liang Wang, Kwang-Ting Tim Cheng
Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i...
August 24, 2017: Medical Image Analysis
Dehui Xiang, Ulas Bagci, Chao Jin, Fei Shi, Weifang Zhu, Jianhua Yao, Milan Sonka, Xinjian Chen
This paper introduces a model-based approach for a fully automatic delineation of kidney and cortex tissue from contrast-enhanced abdominal CT scans. The proposed framework, named CorteXpert, consists of two new strategies for kidney tissue delineation: cortex model adaptation and non-uniform graph search. CorteXpert was validated on a clinical data set of 58 CT scans using the cross-validation evaluation strategy. The experimental results indicated the state-of-the-art segmentation accuracies (as dice coefficient): 97...
August 23, 2017: Medical Image Analysis
Yu Zhao, Qinglin Dong, Hanbo Chen, Armin Iraji, Yujie Li, Milad Makkie, Zhifeng Kou, Tianming Liu
State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases...
August 18, 2017: Medical Image Analysis
Veronika A Zimmer, Ben Glocker, Nadine Hahner, Elisenda Eixarch, Gerard Sanroma, Eduard Gratacós, Daniel Rueckert, Miguel Ángel González Ballester, Gemma Piella
It is challenging to characterize and classify normal and abnormal brain development during early childhood. To reduce the complexity of heterogeneous data population, manifold learning techniques are increasingly applied, which find a low-dimensional representation of the data, while preserving all relevant information. The neighborhood definition used for constructing manifold representations of the population is crucial for preserving the similarity structure and it is highly application dependent. The recently proposed neighborhood approximation forests learn a neighborhood structure in a dataset based on a user-defined distance...
August 9, 2017: Medical Image Analysis
Daniel Toth, Maria Panayiotou, Alexander Brost, Jonathan M Behar, Christopher A Rinaldi, Kawal S Rhode, Peter Mountney
A key component of image guided interventions is the registration of preoperative and intraoperative images. Classical registration approaches rely on cross-modality information; however, in modalities such as MRI and X-ray there may not be sufficient cross-modality information. This paper proposes a fundamentally different registration approach which uses adjacent anatomical structures with superabundant vessel reconstruction and dynamic outlier rejection. In the targeted clinical scenario of cardiac resynchronization therapy (CRT) delivery, preoperative, non contrast-enhanced, MRI is registered to intraoperative, contrasted X-ray fluoroscopy...
August 5, 2017: Medical Image Analysis
Yakui Chu, Jian Yang, Shaodong Ma, Danni Ai, Wenjie Li, Hong Song, Liang Li, Duanduan Chen, Lei Chen, Yongtian Wang
This paper quantifies the registration and fusion display errors of augmented reality-based nasal endoscopic surgery (ARNES). We comparatively investigated the spatial calibration process for front-end endoscopy and redefined the accuracy level of a calibrated endoscope by using a calibration tool with improved structural reliability. We also studied how registration accuracy was combined with the number and distribution of the deployed fiducial points (FPs) for positioning and the measured registration time...
August 3, 2017: Medical Image Analysis
Thomas Küstner, Martin Schwartz, Petros Martirosian, Sergios Gatidis, Ferdinand Seith, Christopher Gilliam, Thierry Blu, Hadi Fayad, Dimitris Visvikis, F Schick, B Yang, H Schmidt, N F Schwenzer
PURPOSE: To develop a motion correction for Positron-Emission-Tomography (PET) using simultaneously acquired magnetic-resonance (MR) images within 90 s. METHODS: A 90 s MR acquisition allows the generation of a cardiac and respiratory motion model of the body trunk. Thereafter, further diagnostic MR sequences can be recorded during the PET examination without any limitation. To provide full PET scan time coverage, a sensor fusion approach maps external motion signals (respiratory belt, ECG-derived respiration signal) to a complete surrogate signal on which the retrospective data binning is performed...
August 3, 2017: Medical Image Analysis
A Benou, R Veksler, A Friedman, T Riklin Raviv
Dynamic contrast-enhanced MRI (DCE-MRI) is an imaging protocol where MRI scans are acquired repetitively throughout the injection of a contrast agent. The analysis of dynamic scans is widely used for the detection and quantification of blood-brain barrier (BBB) permeability. Extraction of the pharmacokinetic (PK) parameters from the DCE-MRI concentration curves allows quantitative assessment of the integrity of the BBB functionality. However, curve fitting required for the analysis of DCE-MRI data is error-prone as the dynamic scans are subject to non-white, spatially-dependent and anisotropic noise...
August 2, 2017: Medical Image Analysis
Xiaoshuang Shi, Fuyong Xing, KaiDi Xu, Yuanpu Xie, Hai Su, Lin Yang
In pathology image analysis, morphological characteristics of cells are critical to grade many diseases. With the development of cell detection and segmentation techniques, it is possible to extract cell-level information for further analysis in pathology images. However, it is challenging to conduct efficient analysis of cell-level information on a large-scale image dataset because each image usually contains hundreds or thousands of cells. In this paper, we propose a novel image retrieval based framework for large-scale pathology image analysis...
August 1, 2017: Medical Image Analysis
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