Showing papers in "Medical Image Analysis in 2017"

TL;DR: 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, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks.

TL;DR: An efficient and effective dense training scheme which joins the processing of adjacent image patches into one pass through the network while automatically adapting to the inherent class imbalance present in the data, and improves on the state-of-the‐art for all three applications.

TL;DR: A fast and accurate fully automatic method for brain tumor segmentation which is competitive both in terms of accuracy and speed compared to the state of the art, and introduces a novel cascaded architecture that allows the system to more accurately model local label dependencies.

TL;DR: The LUNA16 challenge is described, 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, and the results so far are presented.

TL;DR: A head‐to‐head comparison between a state‐of‐the art in mammography CAD system, relying on a manually designed feature set and a Convolutional Neural Network (CNN), aiming for a system that can ultimately read mammograms independently.

TL;DR: An overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015 is provided, along with the method descriptions and evaluation results from the top performing methods.

TL;DR: The proposed 3D DSN is capable of conducting volume‐to‐volume learning and inference, which can eliminate redundant computations and alleviate the risk of over‐fitting on limited training data, and the3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model.

TL;DR: This paper proposes a common evaluation framework for automatic stroke lesion segmentation from MRIP, describes the publicly available datasets, and presents the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES).

TL;DR: A novel deep contour‐aware network (DCAN) under a unified multi‐task learning framework for more accurate detection and segmentation of objects of interest from histology images is proposed.

TL;DR: The proposed data‐driven model, termed the Central Focused Convolutional Neural Networks (CF‐CNN), to segment lung nodules from heterogeneous CT images achieved superior segmentation performance with average dice scores of 82.15% and 80.02% for the two datasets respectively.

TL;DR: In this article, a generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps, showing which pixels in images play a role in the image-level predictions.

TL;DR: This paper proposes and test several deep learning approaches to assess skeletal bone age automatically and shows an average discrepancy between manual and automatic evaluation of about 0.8 years, which is state‐of‐the‐art performance.

TL;DR: A new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data is introduced, producing a methodology that needs small training sets and produces accurate segmentation results.

TL;DR: An integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention is presented and the current state of the art detection, segmentation and classification results for the INbreast dataset are tested.

TL;DR: A comprehensive review of all the different methods proposed by the literature concerning augmented reality in intra-abdominal minimally invasive surgery (also known as laparoscopic surgery) in order to better grasp the current landscape of the field.

TL;DR: The study shows that despite significant progress over the years, the lack of established surgical tool data‐sets, and reference format for performance assessment and method ranking is preventing faster improvement.

TL;DR: A novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis and builds a deep convolutional neural network for clinical decision making, which the authors call ‘Deep Ensemble Sparse Regression Network.’

TL;DR: An analysis of the literature demonstrates a high variability in the methods used to quantify brain shift as well as a wide range in the measured magnitude of the brain shift, depending on the specifics of the intervention.

TL;DR: The efficacy of the proposed feature selection method in enhancing the performances of both clinical scores prediction and disease status identification, compared to the state‐of‐the‐art methods is shown.

TL;DR: An MRI image synthesis algorithm capable of synthesizing full‐head T2w images and FLAIR images and learns the nonlinear intensity mappings for synthesis using innovative features and a multi‐resolution design is described.

TL;DR: This paper parameterize the complete LV segmentation task in terms of the radial distances between the LV centerpoint and the endo‐ and epicardial contours in polar space and demonstrates the effectiveness of convolutional neural network regression paired with domain‐specific features in clinical segmentation.

TL;DR: This paper introduces the first comprehensive survey of the literature about slice-to-volume registration, presenting a categorical study of the algorithms according to an ad-hoc taxonomy and analyzing advantages and disadvantages of every category.

TL;DR: An automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network features and a single‐stage SVM classifier is presented.

TL;DR: An effective deep learning method for bone suppression in single conventional CXR using deep convolutional neural networks (ConvNets) as basic prediction units and a cascade architecture of ConvNets (called CamsNet) to refine progressively the predicted bone gradients in which theconvNets work at successively increased resolutions is presented.

TL;DR: Experimental results demonstrate that the proposed view‐aligned hypergraph learning (VAHL) method outperforms state‐of‐the‐art methods that use incomplete multi‐modality data for AD/MCI diagnosis.

TL;DR: A novel and powerful technique named 3D Adaptive Crisp Active Contour Method (3D ACACM) is proposed for the segmentation of CT lung images, revealing its superiority and competency to segment lungs in CT images.

TL;DR: An automatic CNN‐based framework to classify multiple radiological gradings in lumbar spinal MRIs via a Convolutional Neural Network framework that takes intervertebral disc volumes as inputs and is trained only on disc‐specific class labels is shown.

TL;DR: A novel method to improve airway segmentation in thoracic computed tomography (CT) by detecting and removing leaks is presented, achieving a higher sensitivity at a low false‐positive rate compared to all the state‐of‐the‐art methods that entered in EXACT09, and approaching the performance of the combination of all of them.

TL;DR: The efficacy of the elasticity‐model based state space approach (EBS) for more accurate tracking of the 2‐dimensional motion of the carotid artery wall towards more effective assessment of the status of atherosclerotic disease in the preclinical stage is demonstrated.

TL;DR: A new approach for detecting major differences in brain activities between Autism Spectrum Disorder (ASD) patients and neurotypical subjects using the resting state fMRI is presented and the classification performance obtained is found to be better when compared to earlier studies in the literature.