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Journal of Medical Imaging

Moi Hoon Yap, Manu Goyal, Fatima M Osman, Robert Martí, Erika Denton, Arne Juette, Reyer Zwiggelaar
Multistage processing of automated breast ultrasound lesions recognition is dependent on the performance of prior stages. To improve the current state of the art, we propose the use of end-to-end deep learning approaches using fully convolutional networks (FCNs), namely FCN-AlexNet, FCN-32s, FCN-16s, and FCN-8s for semantic segmentation of breast lesions. We use pretrained models based on ImageNet and transfer learning to overcome the issue of data deficiency. We evaluate our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions...
January 2019: Journal of Medical Imaging
Annika Hänsch, Michael Schwier, Tobias Gass, Tomasz Morgas, Benjamin Haas, Volker Dicken, Hans Meine, Jan Klein, Horst K Hahn
The segmentation of organs at risk is a crucial and time-consuming step in radiotherapy planning. Good automatic methods can significantly reduce the time clinicians have to spend on this task. Due to its variability in shape and low contrast to surrounding structures, segmenting the parotid gland is challenging. Motivated by the recent success of deep learning, we study the use of two-dimensional (2-D), 2-D ensemble, and three-dimensional (3-D) U-Nets for segmentation. The mean Dice similarity to ground truth is <mml:math xmlns:mml="http://www...
January 2019: Journal of Medical Imaging
Yang Lei, Hui-Kuo Shu, Sibo Tian, Jiwoong Jason Jeong, Tian Liu, Hyunsuk Shim, Hui Mao, Tonghe Wang, Ashesh B Jani, Walter J Curran, Xiaofeng Yang
Magnetic resonance imaging (MRI) provides a number of advantages over computed tomography (CT) for radiation therapy treatment planning; however, MRI lacks the key electron density information necessary for accurate dose calculation. We propose a dictionary-learning-based method to derive electron density information from MRIs. Specifically, we first partition a given MR image into a set of patches, for which we used a joint dictionary learning method to directly predict a CT patch as a structured output. Then a feature selection method is used to ensure prediction robustness...
July 2018: Journal of Medical Imaging
Justin J Tse, Joy Dunmore-Buyze, Maria Drangova, David W Holdsworth
Dual-energy microcomputed tomography (DECT) can provide quantitative information about specific materials of interest, facilitating automated segmentation, and visualization of complex three-dimensional tissues. It is possible to implement DECT on currently available preclinical gantry-based cone-beam micro-CT scanners; however, optimal decomposition image quality requires customized spectral shaping (through added filtration), optimized acquisition protocols, and elimination of misregistration artifacts. We present a method for the fabrication of customized x-ray filters-in both shape and elemental composition-needed for spectral shaping...
July 2018: Journal of Medical Imaging
Suneeta Mall, Patrick C Brennan, Claudia Mello-Thoms
Visual search, the process of detecting and identifying objects using eye movements (saccades) and foveal vision, has been studied for identification of root causes of errors in the interpretation of mammograms. The aim of this study is to model visual search behavior of radiologists and their interpretation of mammograms using deep machine learning approaches. Our model is based on a deep convolutional neural network, a biologically inspired multilayer perceptron that simulates the visual cortex and is reinforced with transfer learning techniques...
July 2018: Journal of Medical Imaging
Mia K Markey, Tamara Miner Haygood, Elizabeth A Krupinski
This guest editorial introduces the special section on Medical Image Perceptions and Observer Performance.
July 2018: Journal of Medical Imaging
Elizabeth A Krupinski
Reducing dose increases noise impacting image quality but can be offset by increasing display luminance. Two contrast detail mammography images were obtained at 26 kV and the same distance between detectors, at 45 and 50 mAs resulting in entrance surface doses of 7.09 and 7.88 mGy, respectively. They were processed to make average gray level of the background independent of the dose level while maintaining original SNR. Eight radiologists viewed the images at 420, <mml:math xmlns:mml=""> <mml:mrow> <mml:mn>1000</mml:mn> <mml:mtext>  </mml:mtext> <mml:mi>cd</mml:mi> <mml:mo>/</mml:mo> <mml:msup> <mml:mrow> <mml:mi>m</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> , and SpotView™ a tool that resulted in an average display luminance of <mml:math xmlns:mml="http://www...
July 2018: Journal of Medical Imaging
Lifeng Yu, Liqiang Ren, Zhoubo Li, Shuai Leng, Cynthia H McCollough
Energy-resolved photon-counting-detector CT (PCD-CT) is promising for material decomposition with multiple contrast agents using two or more energy bins. However, corrections for nonidealities of PCDs are required, which are still active research topics. In addition, PCD-CT is also likely to have a very high cost due to the current lack of mass production capabilities. We proposed an alternative approach to perform multienergy CT (MECT), which is achieved by acquiring triple or quadruple x-ray beam measurements on a dual-source CT scanner...
July 2018: Journal of Medical 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
Sivaramakrishnan Rajaraman, Kamolrat Silamut, Md A Hossain, I Ersoy, Richard J Maude, Stefan Jaeger, George R Thoma, Sameer K Antani
Convolutional neural networks (CNNs) have become the architecture of choice for visual recognition tasks. However, these models are perceived as black boxes since there is a lack of understanding of the learned behavior from the underlying task of interest. This lack of transparency is a serious drawback, particularly in applications involving medical screening and diagnosis since poorly understood model behavior could adversely impact subsequent clinical decision-making. Recently, researchers have begun working on this issue and several methods have been proposed to visualize and understand the behavior of these models...
July 2018: Journal of Medical Imaging
Andreu Badal, Matthew Clark, Bahaa Ghammraoui
Mammography is currently the standard imaging modality used to screen women for breast abnormalities, and, as a result, it is a tool of great importance for the early detection of breast cancer. Physical phantoms are commonly used as surrogates of breast tissue to evaluate some aspects of the performance of mammography systems. However, most phantoms do not reproduce the anatomic heterogeneity of real breasts. New fabrication technologies, such as three-dimensional (3-D) printing, have created the opportunity to build more complex, anatomically realistic breast phantoms that could potentially assist in the evaluation of mammography systems...
July 2018: Journal of Medical Imaging
Lonie R Salkowski, Rosemary Russ
The ability to correlate anatomical knowledge and medical imaging is crucial to radiology and as such, should be a critical component of medical education. However, we are hindered in our ability to teach this skill because we know very little about what expert practice looks like, and even less about novices' understanding. Using a unique simulation tool, this research conducted cognitive clinical interviews with experts and novices to explore differences in how they engage in this correlation and the underlying cognitive processes involved in doing so...
July 2018: Journal of Medical Imaging
Weijie Chen, Qi Gong, Brandon D Gallas
The widely used multireader multicase ROC study design for comparing imaging modalities is the fully crossed (FC) design: every reader reads every case of both modalities. We investigate paired split-plot (PSP) designs that may allow for reduced cost and increased flexibility compared with the FC design. In the PSP design, case images from two modalities are read by the same readers, thereby the readings are paired across modalities. However, within each modality, not every reader reads every case. Instead, both the readers and the cases are partitioned into a fixed number of groups and each group of readers reads its own group of cases-a split-plot design...
July 2018: Journal of Medical Imaging
Tamara Miner Haygood, Barry Mullins, Jia Sun, Behrang Amini, Priya Bhosale, Hyunseon C Kang, Tara Sagebiel, Bilal Mujtaba
Frequently, the consensus conclusion after quality assurance conferences in radiology is that whatever mistake was made could have been avoided if more prior images or documents had been consulted. It is generally assumed that anything that was not specifically cited in the report had not been consulted. Is it actually safe to assume that an image or document that is not cited was also not consulted? It is this question that this investigation addresses. In this Institutional Review Board-approved study, one observer watched the board-certified radiologists while they interpreted imaging studies and issued reports...
July 2018: Journal of Medical Imaging
Folami Alamudun, Paige Paulus, Hong-Jun Yoon, Georgia Tourassi
Prior research has shown that physicians' medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the present case...
July 2018: Journal of Medical Imaging
William F Auffermann, Elizabeth A Krupinski, Srini Tridandapani
The goal of this research was to examine whether search pattern training for central line positioning on chest radiographs (CXRs) improves the ability of healthcare trainees and practitioners to identify malpositioned central venous catheters. Two sets of CXRs with central catheters were shown; half of the images contained catheters that were appropriately positioned, half that were malpositioned. Subjects were asked to: mark the tip of the catheter using the simulated radiology workstations, indicate their confidence in tip localization, and state whether the catheter was appropriately positioned or malpositioned...
July 2018: Journal of Medical Imaging
Trafton Drew, Lauren H Williams, Booth Aldred, Marta E Heilbrun, Satoshi Minoshima
What are the costs and consequences of interruptions during diagnostic radiology? The cognitive psychology literature suggests that interruptions lead to an array of negative consequences that could hurt patient outcomes and lead to lower patient throughput. Meanwhile, observational studies have both noted a strikingly high rate of interruptions and rising number of interruptions faced by radiologists. There is some observational evidence that more interruptions could lead to worse patient outcomes: Balint et al...
July 2018: Journal of Medical Imaging
Morteza Modarresi Asem, Iman Sheikh Oveisi, Mona Janbozorgi
Retinal blood vessels indicate some serious health ramifications, such as cardiovascular disease and stroke. Thanks to modern imaging technology, high-resolution images provide detailed information to help analyze retinal vascular features before symptoms associated with such conditions fully develop. Additionally, these retinal images can be used by ophthalmologists to facilitate diagnosis and the procedures of eye surgery. A fuzzy noise reduction algorithm was employed to enhance color images corrupted by Gaussian noise...
July 2018: Journal of Medical Imaging
Ehsan Samei, Marthony Robins, Baiyu Chen, Greeshma Agasthya
Volume of lung nodules is an important biomarker, quantifiable from computed tomography (CT) images. The usefulness of volume quantification, however, depends on the precision of quantification. Experimental assessment of precision is time consuming. A mathematical estimability model was used to assess the quantification precision of CT nodule volumetry in terms of an index ([Formula: see text]), incorporating image noise and resolution, nodule properties, and segmentation software. The noise and resolution were characterized in terms of noise power spectrum and task transfer function...
July 2018: Journal of Medical Imaging
Taylor Brunton Smith, Justin Solomon, Ehsan Samei
This study's purpose was to develop and validate a method to estimate patient-specific detectability indices directly from patients' CT images (i.e., in vivo ). The method extracts noise power spectrum (NPS) and modulation transfer function (MTF) resolution properties from each patient's CT series based on previously validated techniques. These are combined with a reference task function (10-mm disk lesion with [Formula: see text] HU contrast) to estimate detectability indices for a nonprewhitening matched filter observer model...
July 2018: Journal of Medical Imaging
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