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Medical Image Analysis

Weikang Gong, Lin Wan, Wenlian Lu, Liang Ma, Fan Cheng, Wei Cheng, Stefan Grünewald, Jianfeng Feng
The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression, the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking...
April 5, 2018: Medical Image Analysis
Daniel Wesierski, Anna Jezierska
Localizing instrument parts in video-assisted surgeries is an attractive and open computer vision problem. A working algorithm would immediately find applications in computer-aided interventions in the operating theater. Knowing the location of tool parts could help virtually augment visual faculty of surgeons, assess skills of novice surgeons, and increase autonomy of surgical robots. A surgical tool varies in appearance due to articulation, viewpoint changes, and noise. We introduce a new method for detection and pose estimation of multiple non-rigid and robotic tools in surgical videos...
March 30, 2018: Medical Image Analysis
Davis M Vigneault, Amir Pourmorteza, Marvin L Thomas, David A Bluemke, J Alison Noble
Recent improvements in cardiac computed tomography (CCT) allow for whole-heart functional studies to be acquired at low radiation dose (<2mSv) and high-temporal resolution (<100ms) in a single heart beat. Although the extraction of regional functional information from these images is of great clinical interest, there is a paucity of research into the quantification of regional function from CCT, contrasting with the large body of work in echocardiography and cardiac MR. Here we present the Simultaneous Subdivision Surface Registration (SiSSR) method: a fast, semi-automated image analysis pipeline for quantifying regional function from contrast-enhanced CCT...
March 29, 2018: Medical Image Analysis
Ziyue Xu, Mingchen Gao, Georgios Z Papadakis, Brian Luna, Sanjay Jain, Daniel J Mollura, Ulas Bagci
Segmentation, denoising, and partial volume correction (PVC) are three major processes in the quantification of uptake regions in post-reconstruction PET images. These problems are conventionally addressed by independent steps. In this study, we hypothesize that these three processes are dependent; therefore, jointly solving them can provide optimal support for quantification of the PET images. To achieve this, we utilize interactions among these processes when designing solutions for each challenge. We also demonstrate that segmentation can help in denoising and PVC by locally constraining the smoothness and correction criteria...
March 28, 2018: Medical Image Analysis
Karen López-Linares, Nerea Aranjuelo, Luis Kabongo, Gregory Maclair, Nerea Lete, Mario Ceresa, Ainhoa García-Familiar, Iván Macía, Miguel A González Ballester
Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation...
March 27, 2018: Medical Image Analysis
Mihaela Am Constantinescu, Su-Lin Lee, Sabine Ernst, Apit Hemakom, Danilo Mandic, Guang-Zhong Yang
Radiofrequency catheter ablation is one of the commonly available therapeutic methods for patients suffering from cardiac arrhythmias. The prerequisite of successful ablation is sufficient energy delivery at the target site. However, cardiac and respiratory motion, coupled with endocardial irregularities, can cause catheter drift and dispersion of the radiofrequency energy, thus prolonging procedure time, damaging adjacent tissue, and leading to electrical reconnection of temporarily ablated regions. Therefore, positional accuracy and stability of the catheter tip during energy delivery is of great importance for the outcome of the procedure...
March 23, 2018: Medical Image Analysis
Eric Barnhill, Penny J Davies, Cemre Ariyurek, Andreas Fehlner, Jürgen Braun, Ingolf Sack
A new viscoelastic wave inversion method for MRE, called Heterogeneous Multifrequency Direct Inversion (HMDI), was developed which accommodates heterogeneous elasticity within a direct inversion (DI) by incorporating first-order gradients and combining results from a narrow band of multiple frequencies. The method is compared with a Helmholtz-type DI, Multifrequency Dual Elasto-Visco inversion (MDEV), both on ground-truth Finite Element Method simulations at varied noise levels and a prospective in vivo brain cohort of 48 subjects ages 18-65...
March 17, 2018: Medical Image Analysis
Jin Kyu Gahm, Yonggang Shi
Surface mapping methods play an important role in various brain imaging studies from tracking the maturation of adolescent brains to mapping gray matter atrophy patterns in Alzheimer's disease. Popular surface mapping approaches based on spherical registration, however, have inherent numerical limitations when severe metric distortions are present during the spherical parameterization step. In this paper, we propose a novel computational framework for intrinsic surface mapping in the Laplace-Beltrami (LB) embedding space based on Riemannian metric optimization on surfaces (RMOS)...
March 16, 2018: Medical Image Analysis
Nils Gessert, Matthias Schlüter, Alexander Schlaefer
Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to its micrometer range resolution and volumetric field of view. However, OCT image processing is challenging due to speckle noise and reflection artifacts in addition to the images' 3D nature. We address pose estimation from OCT volume data with a new deep learning-based tracking framework...
March 10, 2018: Medical Image Analysis
Fabian Wenzel, Carsten Meyer, Thomas Stehle, Jochen Peters, Susanne Siemonsen, Christian Thaler, Lyubomir Zagorchev
This work presents a novel approach for the rapid segmentation of clinically relevant subcortical brain structures in T1-weighted MRI by utilizing a shape-constrained deformable surface model. In contrast to other approaches for segmenting brain structures, its design allows for parallel segmentation of individual brain structures within a flexible and robust hierarchical framework such that accurate adaptation and volume computation can be achieved within a minute of processing time. Furthermore, adaptation is driven by local and not global contrast, potentially relaxing requirements with respect to preprocessing steps such as bias-field correction...
March 9, 2018: Medical Image Analysis
Devran Ugurlu, Zeynep Firat, Uğur Türe, Gozde Unal
Accurate digital representation of major white matter bundles in the brain is an important goal in neuroscience image computing since the representations can be used for surgical planning, intra-patient longitudinal analysis and inter-subject population connectivity studies. Reconstructing desired fiber bundles generally involves manual selection of regions of interest by an expert, which is subject to user bias and fatigue, hence an automation is desirable. To that end, we first present a novel anatomical representation based on Neighborhood Resolved Fiber Orientation Distributions (NRFOD) along the fibers...
February 27, 2018: Medical Image Analysis
Chunfeng Lian, Jun Zhang, Mingxia Liu, Xiaopeng Zong, Sheng-Che Hung, Weili Lin, Dinggang Shen
Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is challenging and time-consuming for manual delineation of PVSs. Although several automatic/semi-automatic methods, especially the traditional learning-based approaches, have been proposed for segmentation of 3D PVSs, their performance often depends on the hand-crafted image features, as well as sophisticated preprocessing operations prior to segmentation (e...
February 27, 2018: Medical Image Analysis
Sepideh Almasi, Alexandra Lauric, Adel Malek, Eric L Miller
Registration of vascular networks is an indispensable element of prognostic and diagnostic studies that require structural analysis and comparison over time, among different samples, and to a gold standard. However, vascular networks manifest low spatial texture and sparse structural content so that even small variations in their location can make the intensity-based registration inefficient and prone to errors. Motivated by geometrical graph-based models developed in our prior work, we use the shape information in the graph topology sense to enhance the registration performance...
February 24, 2018: Medical Image Analysis
Jonas Pichat, Juan Eugenio Iglesias, Tarek Yousry, Sébastien Ourselin, Marc Modat
Histology permits the observation of otherwise invisible structures of the internal topography of a specimen. Although it enables the investigation of tissues at a cellular level, it is invasive and breaks topology due to cutting. Three-dimensional (3D) reconstruction was thus introduced to overcome the limitations of single-section studies in a dimensional scope. 3D reconstruction finds its roots in embryology, where it enabled the visualisation of spatial relationships of developing systems and organs, and extended to biomedicine, where the observation of individual, stained sections provided only partial understanding of normal and abnormal tissues...
February 21, 2018: Medical Image Analysis
Valerio Varano, Paolo Piras, Stefano Gabriele, Luciano Teresi, Paola Nardinocchi, Ian L Dryden, Concetta Torromeo, Paolo E Puddu
In landmarks-based Shape Analysis size is measured, in most cases, with Centroid Size. Changes in shape are decomposed in affine and non affine components. Furthermore the non affine component can be in turn decomposed in a series of local deformations (partial warps). If the extent of deformation between two shapes is small, the difference between Centroid Size and m-Volume increment is barely appreciable. In medical imaging applied to soft tissues bodies can undergo very large deformations, involving large changes in size...
February 21, 2018: Medical Image Analysis
Donghuan Lu, Karteek Popuri, Gavin Weiguang Ding, Rakesh Balachandar, Mirza Faisal Beg
Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase where memory loss is the main complaint progressively worsening with behavior issues and poor self-care. However, not all individuals clinically diagnosed with MCI progress to AD. A fraction of subjects with MCI either progress to non-AD dementia or remain stable at the MCI stage without progressing to dementia. Although a curative treatment of AD is currently unavailable, it is extremely important to correctly identify the individuals in the MCI phase that will go on to develop AD so that they may benefit from a curative treatment when one becomes available in the near future...
February 21, 2018: Medical Image Analysis
Ana I L Namburete, Weidi Xie, Mohammad Yaqub, Andrew Zisserman, J Alison Noble
Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal position. To this end, we propose a multi-task fully convolutional neural network (FCN) architecture to address the problem of 3D fetal brain localization, structural segmentation, and alignment to a referential coordinate system. Instead of treating these tasks as independent problems, we optimize the network by simultaneously learning features shared within the input data pertaining to the correlated tasks, and later branching out into task-specific output streams...
February 21, 2018: Medical Image Analysis
Mathias Polfliet, Stefan Klein, Wyke Huizinga, Margarethus M Paulides, Wiro J Niessen, Jef Vandemeulebroucke
Image registration is an important task in medical image analysis. Whereas most methods are designed for the registration of two images (pairwise registration), there is an increasing interest in simultaneously aligning more than two images using groupwise registration. Multimodal registration in a groupwise setting remains difficult, due to the lack of generally applicable similarity metrics. In this work, a novel similarity metric for such groupwise registration problems is proposed. The metric calculates the sum of the conditional entropy between each image in the group and a representative template image constructed iteratively using principal component analysis...
February 17, 2018: Medical Image Analysis
Hao Yan, Owen Carmichael, Debashis Paul, Jie Peng
We present a novel method for estimation of the fiber orientation distribution (FOD) function based on diffusion-weighted magnetic resonance imaging (D-MRI) data. We formulate the problem of FOD estimation as a regression problem through spherical deconvolution and a sparse representation of the FOD by a spherical needlets basis that forms a multi-resolution tight frame for spherical functions. This sparse representation allows us to estimate the FOD by ℓ1 -penalized regression under a non-negativity constraint on the estimated FOD...
February 8, 2018: Medical Image Analysis
Bruno Oliveira, Sandro Queirós, Pedro Morais, Helena R Torres, João Gomes-Fonseca, Jaime C Fonseca, João L Vilaça
Anatomical evaluation of multiple abdominal and thoracic organs is generally performed with computed tomography images. Owing to the large field-of-view of these images, automatic segmentation strategies are typically required, facilitating the clinical evaluation. Multi-atlas segmentation (MAS) strategies have been widely used with this process, requiring multiple alignments between the target image and the set of known datasets, and subsequently fusing the alignment results to obtain the final segmentation...
February 2, 2018: Medical Image Analysis
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