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Medical Image Analysis

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https://www.readbyqxmd.com/read/30099151/neural-multi-atlas-label-fusion-application-to-cardiac-mr-images
#1
Heran Yang, Jian Sun, Huibin Li, Lisheng Wang, Zongben Xu
Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network...
July 31, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/30092545/a-computational-method-for-longitudinal-mapping-of-orientation-specific-expansion-of-cortical-surface-in-infants
#2
Jing Xia, Fan Wang, Yu Meng, Zhengwang Wu, Li Wang, Weili Lin, Caiming Zhang, Dinggang Shen, Gang Li
The cortical surface of the human brain expands dynamically and regionally heterogeneously during the first postnatal year. As all primary and secondary cortical folds as well as many tertiary cortical folds are well established at term birth, the cortical surface area expansion during this stage is largely driven by the increase of surface area in two orthogonal orientations in the tangent plane: 1) the expansion parallel to the folding orientation (i.e., increasing the lengths of folds) and 2) the expansion perpendicular to the folding orientation (i...
July 21, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/30081241/segmentation-of-glandular-epithelium-in-colorectal-tumours-to-automatically-compartmentalise-ihc-biomarker-quantification-a-deep-learning-approach
#3
Yves-Rémi Van Eycke, Cédric Balsat, Laurine Verset, Olivier Debeir, Isabelle Salmon, Christine Decaestecker
In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, including usual haematoxylin-eosin (H&E) as well as immunohistochemistry (IHC). The proposed method makes use of Deep Learning and is based on a new convolutional network architecture. Our method achieves better performances than the state of the art on the H&E images of the GlaS challenge contest, whereas it uses only the haematoxylin colour channel extracted by colour deconvolution from the RGB images in order to extend its applicability to IHC...
July 12, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/30031288/measuring-rib-cortical-bone-thickness-and-cross-section-from-ct
#4
Sven A Holcombe, Eunjoo Hwang, Brian A Derstine, Stewart C Wang
This study assesses the ability to measure local cortical bone thickness, and to obtain mechanically relevant properties of rib cross-sections from clinical-resolution computed tomography (CT) scans of human ribs. The study utilized thirty-four sections of ribs published by Perz et al. (2015) in three modalities: standard clinical CT (clinCT), high-resolution clinical CT (HRclinCT), and microCT (µCT). Clinical-resolution images were processed using a Cortical Bone Mapping (CBM) algorithm applied to cross-cortex signals resampled perpendicularly to an initial smooth periosteal border...
July 10, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/30007254/synthesizing-retinal-and-neuronal-images-with-generative-adversarial-nets
#5
He Zhao, Huiqi Li, Sebastian Maurer-Stroh, Li Cheng
This paper aims at synthesizing multiple realistic-looking retinal (or neuronal) images from an unseen tubular structured annotation that contains the binary vessel (or neuronal) morphology. The generated phantoms are expected to preserve the same tubular structure, and resemble the visual appearance of the training images. Inspired by the recent progresses in generative adversarial nets (GANs) as well as image style transfer, our approach enjoys several advantages. It works well with a small training set with as few as 10 training examples, which is a common scenario in medical image analysis...
July 4, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/30007253/weakly-supervised-convolutional-neural-networks-for-multimodal-image-registration
#6
Yipeng Hu, Marc Modat, Eli Gibson, Wenqi Li, Nooshin Ghavami, Ester Bonmati, Guotai Wang, Steven Bandula, Caroline M Moore, Mark Emberton, Sébastien Ourselin, J Alison Noble, Dean C Barratt, Tom Vercauteren
One of the fundamental challenges in supervised learning for multimodal image registration is the lack of ground-truth for voxel-level spatial correspondence. This work describes a method to infer voxel-level transformation from higher-level correspondence information contained in anatomical labels. We argue that such labels are more reliable and practical to obtain for reference sets of image pairs than voxel-level correspondence. Typical anatomical labels of interest may include solid organs, vessels, ducts, structure boundaries and other subject-specific ad hoc landmarks...
July 4, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29966941/slice-level-diffusion-encoding-for-motion-and-distortion-correction
#7
Jana Hutter, Daan J Christiaens, Torben Schneider, Lucilio Cordero-Grande, Paddy J Slator, Maria Deprez, Anthony N Price, J-Donald Tournier, Mary Rutherford, Joseph V Hajnal
Advances in microstructural modelling are leading to growing requirements on diffusion MRI acquisitions, namely sensitivity to smaller structures and better resolution of the geometric orientations. The resulting acquisitions contain highly attenuated images that present particular challenges when there is motion and geometric distortion. This study proposes to address these challenges by breaking with the conventional one-volume-one-encoding paradigm employed in conventional diffusion imaging using single-shot Echo Planar Imaging...
June 25, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29966940/towards-intelligent-robust-detection-of-anatomical-structures-in-incomplete-volumetric-data
#8
Florin C Ghesu, Bogdan Georgescu, Sasa Grbic, Andreas Maier, Joachim Hornegger, Dorin Comaniciu
Robust and fast detection of anatomical structures represents an important component of medical image analysis technologies. Current solutions for anatomy detection are based on machine learning, and are generally driven by suboptimal and exhaustive search strategies. In particular, these techniques do not effectively address cases of incomplete data, i.e., scans acquired with a partial field-of-view. We address these challenges by following a new paradigm, which reformulates the detection task to teaching an intelligent artificial agent how to actively search for an anatomical structure...
June 23, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29936399/3d-freehand-ultrasound-without-external-tracking-using-deep-learning
#9
Raphael Prevost, Mehrdad Salehi, Simon Jagoda, Navneet Kumar, Julian Sprung, Alexander Ladikos, Robert Bauer, Oliver Zettinig, Wolfgang Wein
This work aims at creating 3D freehand ultrasound reconstructions from 2D probes with image-based tracking, therefore not requiring expensive or cumbersome external tracking hardware. Existing model-based approaches such as speckle decorrelation only partially capture the underlying complexity of ultrasound image formation, thus producing reconstruction accuracies incompatible with current clinical requirements. Here, we introduce an alternative approach that relies on a statistical analysis rather than physical models, and use a convolutional neural network (CNN) to directly estimate the motion of successive ultrasound frames in an end-to-end fashion...
June 15, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29935442/automated-sub-cortical-brain-structure-segmentation-combining-spatial-and-deep-convolutional-features
#10
Kaisar Kushibar, Sergi Valverde, Sandra González-Villà, Jose Bernal, Mariano Cabezas, Arnau Oliver, Xavier Lladó
Sub-cortical brain structure segmentation in Magnetic Resonance Images (MRI) has attracted the interest of the research community for a long time as morphological changes in these structures are related to different neurodegenerative disorders. However, manual segmentation of these structures can be tedious and prone to variability, highlighting the need for robust automated segmentation methods. In this paper, we present a novel convolutional neural network based approach for accurate segmentation of the sub-cortical brain structures that combines both convolutional and prior spatial features for improving the segmentation accuracy...
June 15, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29933116/dual-modality-endoscopic-probe-for-tissue-surface-shape-reconstruction-and-hyperspectral-imaging-enabled-by-deep-neural-networks
#11
Jianyu Lin, Neil T Clancy, Ji Qi, Yang Hu, Taran Tatla, Danail Stoyanov, Lena Maier-Hein, Daniel S Elson
Surgical guidance and decision making could be improved with accurate and real-time measurement of intra-operative data including shape and spectral information of the tissue surface. In this work, a dual-modality endoscopic system has been proposed to enable tissue surface shape reconstruction and hyperspectral imaging (HSI). This system centers around a probe comprised of an incoherent fiber bundle, whose fiber arrangement is different at the two ends, and miniature imaging optics. For 3D reconstruction with structured light (SL), a light pattern formed of randomly distributed spots with different colors is projected onto the tissue surface, creating artificial texture...
June 15, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29913433/temporal-and-volumetric-denoising-via-quantile-sparse-image-prior
#12
Franziska Schirrmacher, Thomas Köhler, Jürgen Endres, Tobias Lindenberger, Lennart Husvogt, James G Fujimoto, Joachim Hornegger, Arnd Dörfler, Philip Hoelter, Andreas K Maier
This paper introduces an universal and structure-preserving regularization term, called quantile sparse image (QuaSI) prior. The prior is suitable for denoising images from various medical imaging modalities. We demonstrate its effectiveness on volumetric optical coherence tomography (OCT) and computed tomography (CT) data, which show different noise and image characteristics. OCT offers high-resolution scans of the human retina but is inherently impaired by speckle noise. CT on the other hand has a lower resolution and shows high-frequency noise...
June 6, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29933115/tracing-cell-lineages-in-videos-of-lens-free-microscopy
#13
Markus Rempfler, Valentin Stierle, Konstantin Ditzel, Sanjeev Kumar, Philipp Paulitschke, Bjoern Andres, Bjoern H Menze
In vitro experiments with cultured cells are essential for studying their growth and migration pattern and thus, for gaining a better understanding of cancer progression and its treatment. Recent progress in lens-free microscopy (LFM) has rendered it an inexpensive tool for label-free, continuous live cell imaging, yet there is only little work on analysing such time-lapse image sequences. We propose (1) a cell detector for LFM images based on fully convolutional networks and residual learning, and (2) a probabilistic model based on moral lineage tracing that explicitly handles multiple detections and temporal successor hypotheses by clustering and tracking simultaneously...
June 5, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29890408/disease-prediction-using-graph-convolutional-networks-application-to-autism-spectrum-disorder-and-alzheimer-s-disease
#14
Sarah Parisot, Sofia Ira Ktena, Enzo Ferrante, Matthew Lee, Ricardo Guerrero, Ben Glocker, Daniel Rueckert
Graphs are widely used as a natural framework that captures interactions between individual elements represented as nodes in a graph. In medical applications, specifically, nodes can represent individuals within a potentially large population (patients or healthy controls) accompanied by a set of features, while the graph edges incorporate associations between subjects in an intuitive manner. This representation allows to incorporate the wealth of imaging and non-imaging information as well as individual subject features simultaneously in disease classification tasks...
June 2, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29886268/a-deep-learning-approach-for-real-time-prostate-segmentation-in-freehand-ultrasound-guided-biopsy
#15
Emran Mohammad Abu Anas, Parvin Mousavi, Purang Abolmaesumi
Targeted prostate biopsy, incorporating multi-parametric magnetic resonance imaging (mp-MRI) and its registration with ultrasound, is currently the state-of-the-art in prostate cancer diagnosis. The registration process in most targeted biopsy systems today relies heavily on accurate segmentation of ultrasound images. Automatic or semi-automatic segmentation is typically performed offline prior to the start of the biopsy procedure. In this paper, we present a deep neural network based real-time prostate segmentation technique during the biopsy procedure, hence paving the way for dynamic registration of mp-MRI and ultrasound data...
June 1, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29857330/%C3%AE-net-omega-net-fully-automatic-multi-view-cardiac-mr-detection-orientation-and-segmentation-with-deep-neural-networks
#16
Davis M Vigneault, Weidi Xie, Carolyn Y Ho, David A Bluemke, J Alison Noble
Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): A novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation...
May 22, 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/30072019/special-issue-on-miccai-2017
#17
EDITORIAL
Maxime Descoteaux, Lena Maier-Hein, Alfred Franz, Pierre Jannin, D Louis Collins, Simon Duchesne
No abstract text is available yet for this article.
August 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29990689/a-cortical-shape-adaptive-approach-to-local-gyrification-index
#18
Ilwoo Lyu, Sun Hyung Kim, Jessica B Girault, John H Gilmore, Martin A Styner
The amount of cortical folding, or gyrification, is typically measured within local cortical regions covered by an equidistant geodesic or nearest neighborhood-ring kernel. However, without careful design, such a kernel can easily cover multiple sulcal and gyral regions that may not be functionally related. Furthermore, this can result in smoothing out details of cortical folding, which consequently blurs local gyrification measurements. In this paper, we propose a novel kernel shape to locally quantify cortical gyrification within sulcal and gyral regions...
August 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29990688/deep-learning-and-conditional-random-fields-based-depth-estimation-and-topographical-reconstruction-from-conventional-endoscopy
#19
Faisal Mahmood, Nicholas J Durr
Colorectal cancer is the fourth leading cause of cancer deaths worldwide and the second leading cause in the United States. The risk of colorectal cancer can be mitigated by the identification and removal of premalignant lesions through optical colonoscopy. Unfortunately, conventional colonoscopy misses more than 20% of the polyps that should be removed, due in part to poor contrast of lesion topography. Imaging depth and tissue topography during a colonoscopy is difficult because of the size constraints of the endoscope and the deforming mucosa...
August 2018: Medical Image Analysis
https://www.readbyqxmd.com/read/29852312/the-challenge-of-cerebral-magnetic-resonance-imaging-in-neonates-a-new-method-using-mathematical-morphology-for-the-segmentation-of-structures-including-diffuse-excessive-high-signal-intensities
#20
Yongchao Xu, Baptiste Morel, Sonia Dahdouh, Élodie Puybareau, Alessio Virzì, Héléne Urien, Thierry Géraud, Catherine Adamsbaum, Isabelle Bloch
Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter...
August 2018: Medical Image Analysis
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