Ghesu, Florin C Georgescu, Bogdan Mansoor, Awais Yoo, Youngjin Neumann, Dominik Patel, Pragneshkumar Vishwanath, Reddappagari Suryanarayana Balter, James M Cao, Yue Grbic, Sasa
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Published in
Journal of medical imaging (Bellingham, Wash.)
Building accurate and robust artificial intelligence systems for medical image assessment requires the creation of large sets of annotated training examples. However, constructing such datasets is very costly due to the complex nature of annotation tasks, which often require expert knowledge (e.g., a radiologist). To counter this limitation, we pro...
Liu, Renfei Lauze, François Erleben, Kenny Berg, Rune W Darkner, Sune
Published in
Journal of medical imaging (Bellingham, Wash.)
Applying machine learning techniques to magnetic resonance diffusion-weighted imaging (DWI) data is challenging due to the size of individual data samples and the lack of labeled data. It is possible, though, to learn general patterns from a very limited amount of training data if we take advantage of the geometry of the DWI data. Therefore, we pre...
Wang, Yaning Tiusaba, Laura Jacobs, Shimon Saruwatari, Michele Ning, Bo Levitt, Marc Sandler, Anthony D Nam, So-Hyun Kang, Jin U Cha, Jaepyeong
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Published in
Journal of medical imaging (Bellingham, Wash.)
Intraoperative evaluation of bowel perfusion is currently dependent upon subjective assessment. Thus, quantitative and objective methods of bowel viability in intestinal anastomosis are scarce. To address this clinical need, a conditional adversarial network is used to analyze the data from laser speckle contrast imaging (LSCI) paired with a visibl...
Herbsthofer, Laurin Tomberger, Martina Smolle, Maria A Prietl, Barbara Pieber, Thomas R López-García, Pablo
Published in
Journal of medical imaging (Bellingham, Wash.)
Cell segmentation algorithms are commonly used to analyze large histologic images as they facilitate interpretation, but on the other hand they complicate hypothesis-free spatial analysis. Therefore, many applications train convolutional neural networks (CNNs) on high-resolution images that resolve individual cells instead, but their practical appl...
Paul, Samantha K Pan, Ian Sobol, Warren M
Published in
Journal of medical imaging (Bellingham, Wash.)
To compare the performance of four deep active learning (DAL) approaches to optimize label efficiency for training diabetic retinopathy (DR) classification deep learning models. 88,702 color retinal fundus photographs from 44,351 patients with DR grades from the publicly available EyePACS dataset were used. Four DAL approaches [entropy sampling (ES...
Noothout, Julia M H Lessmann, Nikolas van Eede, Matthijs C van Harten, Louis D Sogancioglu, Ecem Heslinga, Friso G Veta, Mitko van Ginneken, Bram Išgum, Ivana
Published in
Journal of medical imaging (Bellingham, Wash.)
Purpose: Ensembles of convolutional neural networks (CNNs) often outperform a single CNN in medical image segmentation tasks, but inference is computationally more expensive and makes ensembles unattractive for some applications. We compared the performance of differently constructed ensembles with the performance of CNNs derived from these ensembl...
Agnes, Sundaresan A Anitha, Jeevanayagam
Published in
Journal of medical imaging (Bellingham, Wash.)
Purpose: Segmentation of lung nodules in chest CT images is essential for image-driven lung cancer diagnosis and follow-up treatment planning. Manual segmentation of lung nodules is subjective because the approach depends on the knowledge and experience of the specialist. We proposed a multiscale fully convolutional three-dimensional UNet (MF-3D UN...
Kuhlengel, Trevor K Bascom, Rebecca Higgins, William E
Published in
Journal of medical imaging (Bellingham, Wash.)
Purpose: For a patient at risk of having lung cancer, accurate disease staging is vital as it dictates disease prognosis and treatment. Accurate staging requires a comprehensive sampling of lymph nodes within the chest via bronchoscopy. Unfortunately, physicians are generally unable to plan and perform sufficiently comprehensive procedures to ensur...
van Velzen, Sanne G M de Vos, Bob D Noothout, Julia M H Verkooijen, Helena M Viergever, Max A Išgum, Ivana
Published in
Journal of medical imaging (Bellingham, Wash.)
Purpose: Coronary artery calcium (CAC) score, i.e., the amount of CAC quantified in CT, is a strong and independent predictor of coronary heart disease (CHD) events. However, CAC scoring suffers from limited interscan reproducibility, which is mainly due to the clinical definition requiring application of a fixed intensity level threshold for segme...
Couvy-Duchesne, Baptiste Zhang, Futao Kemper, Kathryn E Sidorenko, Julia Wray, Naomi R Visscher, Peter M Colliot, Olivier Yang, Jian
Published in
Journal of medical imaging (Bellingham, Wash.)
Purpose: Covariance between gray-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g., age, head size, and scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass...
