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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
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
Florent Grélard, Fabien Baldacci, Anne Vialard, Jean-Philippe Domenger
This paper presents new methods to study the shape of tubular organs. Determining precise cross-sections is of major importance to perform geometrical measurements, such as diameter, wall-thickness estimation or area measurement. Our first contribution is a robust method to estimate orthogonal planes based on the Voronoi Covariance Measure. Our method is not relying on a curve-skeleton computation beforehand. This means our orthogonal plane estimator can be used either on the skeleton or on the volume. Another important step towards tubular organ characterization is achieved through curve-skeletonization, as skeletons allow to compare two tubular organs, and to perform virtual endoscopy...
July 29, 2017: Medical Image Analysis
Pedro Morais, João L Vilaça, Sandro Queirós, Felix Bourier, Isabel Deisenhofer, João Manuel R S Tavares, Jan D'hooge
Multiple strategies have previously been described for atrial region (i.e. atrial bodies and aortic tract) segmentation. Although these techniques have proven their accuracy, inadequate results in the mid atrial walls are common, restricting their application for specific cardiac interventions. In this work, we introduce a novel competitive strategy to perform atrial region segmentation with correct delineation of the thin mid walls, and integrated it into the B-spline Explicit Active Surfaces framework. A double-stage segmentation process is used, which starts with a fast contour growing followed by a refinement stage with local descriptors...
July 27, 2017: Medical Image Analysis
Yuanpu Xie, Fuyong Xing, Xiaoshuang Shi, Xiangfei Kong, Hai Su, Lin Yang
Efficient and robust cell detection serves as a critical prerequisite for many subsequent biomedical image analysis methods and computer-aided diagnosis (CAD). It remains a challenging task due to touching cells, inhomogeneous background noise, and large variations in cell sizes and shapes. In addition, the ever-increasing amount of available datasets and the high resolution of whole-slice scanned images pose a further demand for efficient processing algorithms. In this paper, we present a novel structured regression model based on a proposed fully residual convolutional neural network for efficient cell detection...
July 26, 2017: Medical Image Analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen A W M van der Laak, Bram van Ginneken, Clara I Sánchez
Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal...
July 26, 2017: Medical Image Analysis
Eli Gibson, Yipeng Hu, Henkjan J Huisman, Dean C Barratt
Segmentation algorithms are typically evaluated by comparison to an accepted reference standard. The cost of generating accurate reference standards for medical image segmentation can be substantial. Since the study cost and the likelihood of detecting a clinically meaningful difference in accuracy both depend on the size and on the quality of the study reference standard, balancing these trade-offs supports the efficient use of research resources. In this work, we derive a statistical power calculation that enables researchers to estimate the appropriate sample size to detect clinically meaningful differences in segmentation accuracy (i...
July 22, 2017: Medical Image Analysis
Amir Jamaludin, Timor Kadir, Andrew Zisserman
The objective of this work is to automatically produce radiological gradings of spinal lumbar MRIs and also localize the predicted pathologies. We show that this can be achieved via a Convolutional Neural Network (CNN) framework that takes intervertebral disc volumes as inputs and is trained only on disc-specific class labels. Our contributions are: (i) a CNN architecture that predicts multiple gradings at once, and we propose variants of the architecture including using 3D convolutions; (ii) showing that this architecture can be trained using a multi-task loss function without requiring segmentation level annotation; and (iii) a localization method that clearly shows pathological regions in the disc volumes...
July 21, 2017: Medical Image Analysis
Andre Mastmeyer, Guillaume Pernelle, Ruibin Ma, Lauren Barber, Tina Kapur
The gynecological cancer mortality rate, including cervical, ovarian, vaginal and vulvar cancers, is more than 20,000 annually in the US alone. In many countries, including the US, external-beam radiotherapy followed by high dose rate brachytherapy is the standard-of-care. The superior ability of MR to visualize soft tissue has led to an increase in its usage in planning and delivering brachytherapy treatment. A technical challenge associated with the use of MRI imaging for brachytherapy, in contrast to that of CT imaging, is the visualization of catheters that are used to place radiation sources into cancerous tissue...
July 18, 2017: Medical Image Analysis
Hanbo Chen, Yujie Li, Fangfei Ge, Gang Li, Dinggang Shen, Tianming Liu
One distinct feature of the cerebral cortex is its convex (gyri) and concave (sulci) folding patterns. Due to the remarkable complexity and variability of gyral/sulcal shapes, it has been challenging to quantitatively model their organization patterns. Inspired by the observation that the lines of gyral crests can form a connected graph on each brain hemisphere, we propose a new representation of cortical gyri/sulci organization pattern - gyral net, which models cortical architecture from a graph perspective, starting with nodes and edges obtained from the reconstructed cortical surfaces...
July 15, 2017: Medical Image Analysis
Arnaud Arindra Adiyoso Setio, Alberto Traverso, Thomas de Bel, Moira S N Berens, Cas van den Bogaard, Piergiorgio Cerello, Hao Chen, Qi Dou, Maria Evelina Fantacci, Bram Geurts, Robbert van der Gugten, Pheng Ann Heng, Bart Jansen, Michael M J de Kaste, Valentin Kotov, Jack Yu-Hung Lin, Jeroen T M C Manders, Alexander Sóñora-Mengana, Juan Carlos García-Naranjo, Evgenia Papavasileiou, Mathias Prokop, Marco Saletta, Cornelia M Schaefer-Prokop, Ernst T Scholten, Luuk Scholten, Miranda M Snoeren, Ernesto Lopez Torres, Jef Vandemeulebroucke, Nicole Walasek, Guido C A Zuidhof, Bram van Ginneken, Colin Jacobs
Automatic detection of pulmonary nodules in thoracic computed tomography (CT) scans has been an active area of research for the last two decades. However, there have only been few studies that provide a comparative performance evaluation of different systems on a common database. We have therefore set up the LUNA16 challenge, an objective evaluation framework for automatic nodule detection algorithms using the largest publicly available reference database of chest CT scans, the LIDC-IDRI data set. In LUNA16, participants develop their algorithm and upload their predictions on 888 CT scans in one of the two tracks: 1) the complete nodule detection track where a complete CAD system should be developed, or 2) the false positive reduction track where a provided set of nodule candidates should be classified...
July 13, 2017: Medical Image Analysis
Miaomiao Zhang, William M Wells, Polina Golland
We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space...
July 8, 2017: Medical Image Analysis
Shuo Wang, Mu Zhou, Zaiyi Liu, Zhenyu Liu, Dongsheng Gu, Yali Zang, Di Dong, Olivier Gevaert, Jie Tian
Accurate lung nodule segmentation from computed tomography (CT) images is of great importance for image-driven lung cancer analysis. However, the heterogeneity of lung nodules and the presence of similar visual characteristics between nodules and their surroundings make it difficult for robust nodule segmentation. In this study, we propose a data-driven model, termed the Central Focused Convolutional Neural Networks (CF-CNN), to segment lung nodules from heterogeneous CT images. Our approach combines two key insights: 1) the proposed model captures a diverse set of nodule-sensitive features from both 3-D and 2-D CT images simultaneously; 2) when classifying an image voxel, the effects of its neighbor voxels can vary according to their spatial locations...
June 30, 2017: Medical Image Analysis
Ricardo Coronado-Leija, Alonso Ramirez-Manzanares, Jose Luis Marroquin
A stable, accurate and robust-to-noise method for the estimation of the intra-voxel bundle-wise diffusion properties for diffusion-weighted magnetic resonance imaging is presented. The proposed method overcomes some of the limitations of most of the multi-fiber algorithms in the literature and extends them to estimate the diffusion profiles, improving the estimation of the intra-voxel geometry at challenging microstructure configurations, that is to say: relatively small crossing angles, different voxel-wise anisotropic diffusion profiles and low SNR...
June 29, 2017: Medical Image Analysis
Alborz Amir-Khalili, Ghassan Hamarneh, Rafeef Abugharbieh
Identification of vascular structures from medical images is integral to many clinical procedures. Most vessel segmentation techniques ignore the characteristic pulsatile motion of vessels in their formulation. In a recent effort to automatically segment vessels that are hidden under fat, we motivated the use of the magnitude of local pulsatile motion extracted from surgical endoscopic video. In this article we propose a new approach that leverages the local orientation, in addition to magnitude of motion, and demonstrate that the extended computation and utilization of motion vectors can improve the segmentation of vascular structures...
June 29, 2017: Medical Image Analysis
Sebastien Ourselin, Mert R Sabuncu, William Wells, Leo Joskowicz, Gozde Unal, Andreas Maier
No abstract text is available yet for this article.
June 29, 2017: Medical Image Analysis
Dietmar Cordes, Zhengshi Yang, Xiaowei Zhuang, Karthik Sreenivasan, Virendra Mishra, Le H Hua
A difficult problem in quantitative MRI is the accurate determination of the proton density, which is an important quantity in measuring brain tissue organization. Recent progress in estimating proton density in vivo has been based on using the inverse linear relationship between the longitudinal relaxation rate T1 and proton density. In this study, the same type of relationship is being used, however, in a more general framework by constructing 3D basis functions to model the receiver bias field. The novelty of this method is that the basis functions developed are suitable to cover an entire range of inverse linearities between T1 and proton density...
June 23, 2017: Medical Image Analysis
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