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IEEE Transactions on Medical Imaging

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https://www.readbyqxmd.com/read/30403625/data-driven-elasticity-imaging-using-cartesian-neural-network-constitutive-models-and-the-autoprogressive-method
#1
Cameron Hoerig, Jamshid Ghaboussi, Michael F Insana
Quasi-static elasticity imaging techniques rely on model-based mathematical inverse methods to estimate mechanical parameters from force-displacement measurements. These techniques introduce simplifying assumptions that preclude exploration of unknown mechanical properties with potential diagnostic value. We previously reported a data-driven approach to elasticity imaging using artificial neural networks (NNs) that circumvents limitations associated with model-based inverse methods. NN constitutive models can learn stress-strain behavior from forcedisplacement measurements using the Autoprogressive method (AutoP) without prior assumptions of the underlying constitutive model...
November 5, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30403622/phase-linearity-measurement-a-novel-index-for-brain-functional-connectivity
#2
Fabio Baselice, Antonietta Sorriso, Rosaria Rucco, Pierpaolo Sorrentino
The problem of describing how different brain areas interact between each other has been granted a great deal of attention in the last years. The idea that neuronal ensembles behave as oscillators and that they communicate through synchronization is now widely accepted. To this regard, EEG and MEG provide the signals that allow the estimation of such communication in vivo. Hence, phase-based metrics are essential. However, the application of phased-based metrics for measuring brain connectivity has proved problematic so far, since they appear to be less resilient to noise as compared to amplitude-based ones...
November 5, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30403624/unsupervised-feature-extraction-via-deep-learning-for-histopathological-classification-of-colon-tissue-images
#3
Can Taylan Sari, Cigdem Gunduz-Demir
Histopathological examination is today's gold standard for cancer diagnosis. However, this task is time consuming and prone to errors as it requires a detailed visual inspection and interpretation of a pathologist. Digital pathology aims at alleviating these problems by providing computerized methods that quantitatively analyze digitized histopathological tissue images. The performance of these methods mainly rely on features that they use, and thus, their success strictly depends on the ability of these features successfully quantifying the histopathology domain...
November 2, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30403623/learning-based-quality-control-for-cardiac-mr-images
#4
Giacomo Tarroni, Ozan Oktay, Wenjia Bai, Andreas Schuh, Hideaki Suzuki, Jonathan Passerat-Palmbach, Antonio de Marvao, Declan P O'Regan, Stuart Cook, Ben Glocker, Paul M Matthews, Daniel Rueckert
The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artefacts such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images: however, this procedure is strongly operatordependent, cumbersome and sometimes incompatible with the time constraints in clinical settings and large-scale studies...
November 1, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30387726/hyperdense-net-a-hyper-densely-connected-cnn-for-multi-modal-image-segmentation
#5
Jose Dolz, Karthik Gopinath, Jing Yuan, Herve Lombaert, Christian Desrosiers, Ismail Ben Ayed
Recently, dense connections have attracted substantial attention in computer vision because they facilitate gradient flow and implicit deep supervision during training. Particularly, DenseNet, which connects each layer to every other layer in a feed-forward fashion, has shown impressive performances in natural image classification tasks. We propose HyperDenseNet, a 3D fully convolutional neural network that extends the definition of dense connectivity to multi-modal segmentation problems. Each imaging modality has a path, and dense connections occur not only between the pairs of layers within the same path, but also between those across different paths...
October 30, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30371359/structure-based-intensity-propagation-for-3d-brain-reconstruction-with-multilayer-section-microscopy
#6
Haoyi Liang, Natalia Dabrowska, Jaideep Kapur, Daniel S Weller
Microscopy is widely used for brain research because of its high resolution and ability to stain for many different biomarkers. Since whole brains are usually sectioned for tissue staining and imaging, reconstruction of 3D brain volumes from these sections is important for visualization and analysis. Recently developed tissue clearing techniques and advanced confocal microscopy enable multilayer sections to be imaged without compromising the resolution. However, noticeable structure inconsistence occurs if surface layers are used to align these sections...
October 29, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30371358/pulmonary-ct-registration-through-supervised-learning-with-convolutional-neural-networks
#7
Koen A J Eppenhof, Josien P W Pluim
Deformable image registration can be time consuming and often needs extensive parameterization to perform well on a specific application. We present a deformable registration method based on a three-dimensional convolutional neural network, together with a framework for training such a network. The network directly learns transformations between pairs of three-dimensional images. The network is trained on synthetic random transformations, which are applied to a small set of representative images for the desired application...
October 26, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30371357/non-local-low-rank-cube-based-tensor-factorization-for-spectral-ct-reconstruction
#8
Weiwen Wu, Fenglin Liu, Yanbo Zhang, Qian Wang, Hengyong Yu
Spectral computed tomography (CT) reconstructs material-dependent attenuation images from the projections of multiple narrow energy windows which is meaningful for material identification and decomposition. Unfortunately, the multi-energy projection datasets usually have lower signal-noise-ratios (SNR). Very recently, a spatial-spectral cube matching frame (SSCMF) was proposed to explore the non-local spatial-spectral similarities for spectral CT. This method constructs a group by clustering up a series of non-local spatial-spectral cubes...
October 26, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30371356/rsdnet-learning-to-predict-remaining-surgery-duration-from-laparoscopic-videos-without-manual-annotations
#9
Andru Putra Twinanda, Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Accurate surgery duration estimation is necessary for optimal OR planning, which plays an important role in patient comfort and safety as well as resource optimization. It is, however, challenging to preoperatively predict surgery duration since it varies significantly depending on the patient condition, surgeon skills, and intraoperative situation. In this paper, we propose a deep learning pipeline, referred to as RSDNet, which automatically estimates the remaining surgery duration (RSD) intraoperatively by using only visual information from laparoscopic videos...
October 25, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30369441/recognizing-brain-states-using-deep-sparse-recurrent-neural-network
#10
Han Wang, Shijie Zhao, Qinglin Dong, Yan Cui, Yaowu Chen, Junwei Han, Li Xie, Tianming Liu
Brain activity is a dynamic combination of different sensory responses and thus brain activity/state is continuously changing over time. However, the brain's dynamical functional states recognition at fast time-scales in task fMRI data have been rarely explored. In this work, we propose a novel 5-layer deep sparse recurrent neural network (DSRNN) model to accurately recognize the brain states across the whole scan session. Specifically, the DSRNN model includes an input layer, one fully-connected layer, two recurrent layers and a softmax output layer...
October 23, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30369440/evaluation-of-a-quantitative-method-for-carpal-motion-analysis-using-clinical-3d-and-4d-ct-protocols
#11
Johannes G G Dobbe, Marieke G A de Roo, Jim C Visschers, Simon D Strackee, Geert J Streekstra
For wrist complaints related to motion, a 2D radiograph or CT scan of the static wrist may not always be considered diagnostic. Three-dimensional motion imaging, i.e., multiple 3DCT scans in time (4DCT), enables quantifying carpal motion and comparing motion patterns of the affected wrist with those of the healthy contralateral side. The accuracy and precision of the method however, is limited by noise and motion artifacts. Although the technique is considered promising in existing literature, the accuracy and precision of carpal motion analysis has never been investigated systematically...
October 23, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30346281/unsupervised-identification-of-disease-marker-candidates-in-retinal-oct-imaging-data
#12
Philipp Seebock, Sebastian M Waldstein, Sophie Klimscha, Hrvoje Bogunovic, Thomas Schlegl, Bianca S Gerendas, Rene Donner, Ursula Schmidt-Erfurth, Georg Langs
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection and exploitation of findings that are known a priori after annotation of training examples by experts. However, supervision does not scale well, due to the amount of necessary training examples, and the limitation of the marker vocabulary to known entities. In this proof-of-concept study, we propose unsupervised identification of anomalies as candidates for markers in retinal Optical Coherence Tomography (OCT) imaging data without a constraint to a priori definitions...
October 22, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30346280/robust-energy-calibration-technique-for-photon-counting-spectral-detectors
#13
Stefano Vespucci, Chan Soo Park, Raul Torrico, Mini Das
This paper describes the implementation of a novel and robust threshold energy calibration method for photon counting detectors using polychromatic x-ray tubes. Methods often used for such energy calibration may require re-orientation of the detector or introduce calibration errors that are flux and acquisition time dependent. Our newly proposed "differential intensity ratios" (DIR) method offers a practical and robust alternative to existing methods. We demonstrate this robustness against photon flux used in calibration, spectral errors such as pulse pile-up as well as the detector's inherent spectral resolution limits...
October 22, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30334789/automatic-needle-segmentation-and-localization-in-mri-with-3d-convolutional-neural-networks-application-to-mri-targeted-prostate-biopsy
#14
Alireza Mehrtash, Mohsen Ghafoorian, Guillaume Pernelle, Alireza Ziaei, Friso G Heslinga, Kemal Tuncali, Andriy Fedorov, Ron Kikinis, Clare M Tempany, William M Wells, Purang Abolmaesumi, Tina Kapur
Image-guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application, and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications...
October 18, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30334788/synseg-net-synthetic-segmentation-without-target-modality-ground-truth
#15
Yuankai Huo, Zhoubing Xu, Hyeonsoo Moon, Shunxing Bao, Albert Assad, Tamara K Moyo, Michael R Savona, Richard G Abramson, Bennett A Landman
A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually traced images in one imaging modality (e.g., MRI) are able to train a segmentation network for another imaging modality (e.g., CT). In this paper, we propose an end-to-end synthetic segmentation network (SynSeg-Net) to train a segmentation network for a target imaging modality without having manual labels...
October 17, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30334787/geometric-validation-of-continuous-finely-sampled-3d-reconstructions-from-aoct-and-ct-in-upper-airway-models
#16
Hillel B Price, Julia S Kimbell, Ruofei Bu, Amy L Oldenburg
Identification and treatment of obstructive airway disorders (OADs) is greatly aided by imaging of the geometry of the airway lumen. Anatomical optical coherence tomography (aOCT) is a promising high-speed and minimally-invasive endoscopic imaging modality for providing micrometer-resolution scans of the upper airway. Resistance to airflow in OADs is directly caused by reduction in luminal cross-sectional area (CSA). It is hypothesized that aOCT can produce airway CSA measurements as accurate as that from computed tomography (CT)...
October 17, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30334786/knowledge-based-collaborative-deep-learning-for-benign-malignant-lung-nodule-classification-on-chest-ct
#17
Yutong Xie, Yong Xia, Jianpeng Zhang, Yang Song, Dagan Feng, Michael Fulham, Weidong Cai
The accurate identification of malignant lung nodules on chest CT is critical for the early detection of lung cancer, which also offers patients the best chance of cure. Deep learning methods have recently been successfully introduced to computer vision problems, although substantial challenges remain in the detection of malignant nodules due to the lack of large training datasets. In this paper, we propose a multi-view knowledge-based collaborative (MV-KBC) deep model to separate malignant from benign nodules using limited chest CT data...
October 17, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30334785/calibration-free-b0-correction-of-epi-data-using-structured-low-rank-matrix-recovery
#18
Arvind Balachandrasekaran, Merry Mani, Mathews Jacob
We introduce a structured low rank algorithm for the calibration-free compensation of field inhomogeneity artifacts in Echo Planar Imaging (EPI) MRI data. We acquire the data using two EPI readouts that differ in echo-time (TE). Using time segmentation, we reformulate the field inhomogeneity compensation problem as the recovery of an image time series from highly undersampled Fourier measurements. The temporal profile at each pixel is modeled as a single exponential, which is exploited to fill in the missing entries...
October 16, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30334753/reconstruction-of-optical-vector-fields-with-applications-in-endoscopic-imaging
#19
Milana Gataric, George S D Gordon, Francesco Renna, Alberto Gil C P Ramos, Maria P Alcolea, Sarah E Bohndiek
We introduce a framework for the reconstruction of the amplitude, phase and polarisation of an optical vector-field using measurements acquired by an imaging device characterised by an integral transform with an unknown spatially-variant kernel. By incorporating effective regularisation terms, this new approach is able to recover an optical vector-field with respect to an arbitrary representation system, which may be different from the one used for device calibration. In particular, it enables the recovery of an optical vector-field with respect to a Fourier basis, which is shown to yield indicative features of increased scattering associated with tissue abnormalities...
October 12, 2018: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/30334752/path-r-cnn-for-prostate-cancer-diagnosis-and-gleason-grading-of-histological-images
#20
Wenyuan Li, Jiayun Li, Karthik V Sarma, King Chung Ho, Shiwen Shen, Beatrice S Knudsen, Arkadiusz Gertych, Corey W Arnold
Prostate cancer is the most common and second most deadly form of cancer in men in the United States. The classification of prostate cancers based on Gleason grading using histological images is important in risk assessment and treatment planning for patients. Here, we demonstrate a new region-based convolutional neural network (R-CNN) framework for multitask prediction using a Epithelial Network Head and a Grading Network Head. Compared to a single task model, our multi-task model can provide complementary contextual information, which contributes to better performance...
October 12, 2018: IEEE Transactions on Medical Imaging
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