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Deep Learning for medical image processing

Hyoung Suk Park, Sung Min Lee, Hwa Pyung Kim, Jin Keun Seo, Yong Eun Chung
PURPOSE: This paper proposes a sinogram-consistency learning method to deal with beam-hardening related artifacts in polychromatic computerized tomography (CT). The presence of highly attenuating materials in the scan field causes an inconsistent sinogram, that does not match the range space of the Radon transform. When the mismatched data are entered into the range space during CT reconstruction, streaking and shading artifacts are generated owing to the inherent nature of the inverse Radon transform METHODS: The proposed learning method aims to repair inconsistent sinogram by removing the primary metal-induced beam-hardening factors along the metal trace in the sinogram...
September 20, 2018: Medical Physics
Sebastien Jean Mambou, Petra Maresova, Ondrej Krejcar, Ali Selamat, Kamil Kuca
Women's breasts are susceptible to developing cancer; this is supported by a recent study from 2016 showing that 2.8 million women worldwide had already been diagnosed with breast cancer that year. The medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. Over the past 20 years several techniques have been proposed for this purpose, such as mammography, which is frequently used for breast cancer diagnosis...
August 25, 2018: Sensors
Seyed Sadegh Mohseni Salehi, Shadab Khan, Deniz Erdogmus, Ali Gholipour
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-totemplate 3D rigid registration, we propose deep learning-based methods that are trained to find the 3D position of arbitrarily oriented subjects or anatomy in a canonical space based on slices or volumes of medical images. For this, we propose regression convolutional neural networks (CNNs) that learn to predict the angle-axis representation of 3D rotations and translations using image features...
August 21, 2018: IEEE Transactions on Medical Imaging
Bihan Wen, Saiprasad Ravishankar, Yoram Bresler
Techniques exploiting the sparsity of images in a transform domain are effective for various applications in image and video processing. In particular, transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction. Recently, we proposed methods for online learning of sparsifying transforms from streaming signals, which enjoy good convergence guarantees, and involve lower computational costs than online synthesis dictionary learning...
August 16, 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Natalie Stephenson, Emily Shane, Jessica Chase, Jason Rowland, David Ries, Nicola Justice, Jie Zhang, Leong Chan, Renzhi Cao
Drug discovery, which is the process of discovering new candidate medications, is very important for pharmaceutical industries. At its current stage, discovering new drugs is still an expensive and time-consuming process, requiring Phases I, II and III for clinical trials. Recently, machine learning techniques in Artificial Intelligence (AI), especially the deep learning techniques which allow a computational model to generate multiple layers, have been widely applied and achieved state-of-the-art performance in various fields, such as speech recognition, image classification, bioinformatics, etc...
August 19, 2018: Current Drug Metabolism
Filippo Pesapane, Caterina Volonté, Marina Codari, Francesco Sardanelli
Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place...
August 15, 2018: Insights Into Imaging
Ke Yan, Xiaosong Wang, Le Lu, Ronald M Summers
Extracting, harvesting, and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. Meanwhile, vast amounts of clinical annotations have been collected and stored in hospitals' picture archiving and communication systems (PACS). These types of annotations, also known as bookmarks in PACS, are usually marked by radiologists during their daily workflow to highlight significant image findings that may serve as reference for later studies. We propose to mine and harvest these abundant retrospective medical data to build a large-scale lesion image dataset...
July 2018: Journal of Medical Imaging
Feiwei Qin, Nannan Gao, Yong Peng, Zizhao Wu, Shuying Shen, Artur Grudtsin
BACKGROUND AND OBJECTIVE: Leukocyte classification and cytometry have wide applications in medical domain, previous researches usually exploit machine learning techniques to classify leukocytes automatically. However, constrained by the past development of machine learning techniques, for example, extracting distinctive features from raw microscopic images are difficult, the widely used SVM classifier only has relative few parameters to tune, these methods cannot efficiently handle fine-grained classification cases when the white blood cells have up to 40 categories...
August 2018: Computer Methods and Programs in Biomedicine
Ge Wang, Jong Chu Ye, Klaus Mueller, Jeffrey A Fessler
Over past several years, machine learning, or more generally artificial intelligence, has generated overwhelming research interest and attracted unprecedented public attention. As tomographic imaging researchers, we share the excitement from our imaging perspective [item 1) in the Appendix], and organized this special issue dedicated to the theme of "Machine learning for image reconstruction." This special issue is a sister issue of the special issue published in May 2016 of this journal with the theme "Deep learning in medical imaging" [item 2) in the Appendix]...
June 2018: IEEE Transactions on Medical Imaging
Mattias P Heinrich, Max Blendowski, Ozan Oktay
PURPOSE: Deep convolutional neural networks (DCNN) are currently ubiquitous in medical imaging. While their versatility and high-quality results for common image analysis tasks including segmentation, localisation and prediction is astonishing, the large representational power comes at the cost of highly demanding computational effort. This limits their practical applications for image-guided interventions and diagnostic (point-of-care) support using mobile devices without graphics processing units (GPU)...
September 2018: International Journal of Computer Assisted Radiology and Surgery
Zhexin Jiang, Hao Zhang, Yi Wang, Seok-Bum Ko
Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems. In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging...
September 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Xingxing Zhu, Mingyue Ding, Tao Huang, Xiaomeng Jin, Xuming Zhang
Nonrigid multimodal image registration remains a challenging task in medical image processing and analysis. The structural representation (SR)-based registration methods have attracted much attention recently. However, the existing SR methods cannot provide satisfactory registration accuracy due to the utilization of hand-designed features for structural representation. To address this problem, the structural representation method based on the improved version of the simple deep learning network named PCANet is proposed for medical image registration...
May 8, 2018: Sensors
Xu Han, Roland Kwitt, Stephen Aylward, Spyridon Bakas, Bjoern Menze, Alexander Asturias, Paul Vespa, John Van Horn, Marc Niethammer
Brain extraction from 3D medical images is a common pre-processing step. A variety of approaches exist, but they are frequently only designed to perform brain extraction from images without strong pathologies. Extracting the brain from images exhibiting strong pathologies, for example, the presence of a brain tumor or of a traumatic brain injury (TBI), is challenging. In such cases, tissue appearance may substantially deviate from normal tissue appearance and hence violates algorithmic assumptions for standard approaches to brain extraction; consequently, the brain may not be correctly extracted...
August 1, 2018: NeuroImage
Paras Lakhani, Daniel L Gray, Carl R Pett, Paul Nagy, George Shih
There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects...
May 3, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Vijaya B Kolachalama, Priyamvada Singh, Christopher Q Lin, Dan Mun, Mostafa E Belghasem, Joel M Henderson, Jean M Francis, David J Salant, Vipul C Chitalia
Introduction: Chronic kidney damage is routinely assessed semiquantitatively by scoring the amount of fibrosis and tubular atrophy in a renal biopsy sample. Although image digitization and morphometric techniques can better quantify the extent of histologic damage, we need more widely applicable ways to stratify kidney disease severity. Methods: We leveraged a deep learning architecture to better associate patient-specific histologic images with clinical phenotypes (training classes) including chronic kidney disease (CKD) stage, serum creatinine, and nephrotic-range proteinuria at the time of biopsy, and 1-, 3-, and 5-year renal survival...
March 2018: KI Reports
Tobias Ross, David Zimmerer, Anant Vemuri, Fabian Isensee, Manuel Wiesenfarth, Sebastian Bodenstedt, Fabian Both, Philip Kessler, Martin Wagner, Beat Müller, Hannes Kenngott, Stefanie Speidel, Annette Kopp-Schneider, Klaus Maier-Hein, Lena Maier-Hein
PURPOSE: Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field. The purpose of this paper was to investigate the concept of self-supervised learning to address this issue...
June 2018: International Journal of Computer Assisted Radiology and Surgery
Jang Hyung Lee, Kwang Gi Kim
Objectives: A diagnostic need often arises to estimate bone age from X-ray images of the hand of a subject during the growth period. Together with measured physical height, such information may be used as indicators for the height growth prognosis of the subject. We present a way to apply the deep learning technique to medical image analysis using hand bone age estimation as an example. Methods: Age estimation was formulated as a regression problem with hand X-ray images as input and estimated age as output...
January 2018: Healthcare Informatics Research
Hiba Chougrad, Hamid Zouaki, Omar Alheyane
BACKGROUND AND OBJECTIVE: Radiologists often have a hard time classifying mammography mass lesions which leads to unnecessary breast biopsies to remove suspicions and this ends up adding exorbitant expenses to an already burdened patient and health care system. METHODS: In this paper we developed a Computer-aided Diagnosis (CAD) system based on deep Convolutional Neural Networks (CNN) that aims to help the radiologist classify mammography mass lesions. Deep learning usually requires large datasets to train networks of a certain depth from scratch...
April 2018: Computer Methods and Programs in Biomedicine
Simukayi Mutasa, Peter D Chang, Carrie Ruzal-Shapiro, Rama Ayyala
Bone age assessment (BAA) is a commonly performed diagnostic study in pediatric radiology to assess skeletal maturity. The most commonly utilized method for assessment of BAA is the Greulich and Pyle method (Pediatr Radiol 46.9:1269-1274, 2016; Arch Dis Child 81.2:172-173, 1999) atlas. The evaluation of BAA can be a tedious and time-consuming process for the radiologist. As such, several computer-assisted detection/diagnosis (CAD) methods have been proposed for automation of BAA. Classical CAD tools have traditionally relied on hard-coded algorithmic features for BAA which suffer from a variety of drawbacks...
August 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Toshiaki Hirasawa, Kazuharu Aoyama, Tetsuya Tanimoto, Soichiro Ishihara, Satoki Shichijo, Tsuyoshi Ozawa, Tatsuya Ohnishi, Mitsuhiro Fujishiro, Keigo Matsuo, Junko Fujisaki, Tomohiro Tada
BACKGROUND: Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. METHODS: A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer...
July 2018: Gastric Cancer
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