8 research outputs found
A radial self-calibrated (RASCAL) generalized autocalibrating partially parallel acquisition (GRAPPA) method using weight interpolation
No full textVideo Event Detection Using Temporal Pyramids of Visual Semantics with Kernel Optimization and Model Subspace Boosting
No full textUnsupervised Learning of Endoscopy Video Frames’ Correspondences from Global and Local Transformation
No full textOR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis
No full textThe OR 2.0 papers cover a wide range of topics such as machine vision and perception, robotics, surgical simulation and modeling, multi-modal data fusion and visualization, image analysis, advanced imaging, advanced display technologies, human-computer interfaces, sensors. The CARE papers cover topics to advance the field of computer-assisted and robotic endoscopy. The CLIP papers cover topics to fill gaps between basic science and clinical applications. The ISIC papers cover topics to facilitate knowledge dissemination in the field of skin image analysis, as well as to host a melanoma detection challenge, raising awareness and interest for these socially valuable tasks
Expert agreement on the presence and spatial localization of melanocytic features in dermoscopy
No full text: Dermoscopy aids in melanoma detection; however, agreement on dermoscopic features, including those of high clinical relevance, remains poor. Herein we attempted to evaluate agreement among experts on 'exemplar images' not only for the presence of melanocytic-specific features but also for spatial localization. This was a cross-sectional, multicenter, observational study. Dermoscopy images exhibiting at least one of 31 melanocytic-specific features were submitted by 25 world experts as 'exemplars'. Using a web-based platform that allows for image mark-up of specific contrast-defined regions (superpixels), 20 expert readers annotated 248 dermoscopic images in collections of 62 images. Each collection was reviewed by five independent readers. A total of 4,507 feature observations were performed. Good-to-excellent agreement was found for 14 of 31 features (45.2%), with 8 achieving excellent agreement (Gwet's AC >0.75) and 7 of them being melanoma-specific features. These features were: 'Peppering /Granularity' (0.91); 'Shiny White Streaks' (0.89); 'Typical Pigment network' (0.83); 'Blotch Irregular' (0.82); 'Negative Network' (0.81); 'Irregular Globules' (0.78); 'Dotted Vessels' (0.77) and 'Blue Whitish Veil' (0.76). By utilizing an exemplar dataset, good-to-excellent agreement was found for 14 features that have previously been shown useful in discriminating nevi from melanoma. All images are public (www.isic-archive.com) and can be used for education, scientific communication and machine learning experiments
Improved Left Ventricular Mass Quantification with Partial Voxel Interpolation – In-Vivo and Necropsy Validation of a Novel Cardiac MRI Segmentation Algorithm
No full textBackground—CMR typically quantifies LV mass (LVM) via manual planimetry (MP), but this approach is time consuming and does not account for partial voxel components - myocardium admixed with blood in a single voxel. Automated segmentation (AS) can account for partial voxels, but this has not been used for LVM quantification. This study used automated CMR segmentation to test the influence of partial voxels on quantification of LVM. Methods and Results—LVM was quantified by AS and MP in 126 consecutive patients and 10 laboratory animals undergoing CMR. AS yielded both partial voxel (ASPV) and full voxel (ASFV) measurements. Methods were independently compared to LVM quantified on echocardiography (echo) and an ex-vivo standard of LVM at necropsy. AS quantified LVM in all patients, yielding a 12-fold decrease in processing time vs. MP (0:21±0:04 vs. 4:18±1:02 min; pFV mass (136±35gm) was slightly lower than MP (139±35; Δ=3±9gm, pPV yielded higher LVM (159±38gm) than MP (Δ=20±10gm) and ASFV (Δ=23±6gm, both pPV and ASFV correlated with larger voxel size (partial r=0.37, pPV yielded better agreement with echo (Δ=20±25gm) than did ASFV (Δ=43±24gm) or MP (Δ=40±22gm, both pPV and ex-vivo results were similar (Δ=1±3gm, p=0.3), whereas ASFV (6±3g, P\u3c0.001) and MP (4±5 g, P=0.02) yielded small but significant differences with LVM at necropsy
Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images
No full textComputer vision may aid in melanoma detection.
We sought to compare melanoma diagnostic accuracy of computer algorithms to dermatologists using dermoscopic images.
We conducted a cross-sectional study using 100 randomly selected dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an international computer vision melanoma challenge dataset (n = 379), along with individual algorithm results from 25 teams. We used 5 methods (nonlearned and machine learning) to combine individual automated predictions into “fusion” algorithms. In a companion study, 8 dermatologists classified the lesions in the 100 images as either benign or malignant.
The average sensitivity and specificity of dermatologists in classification was 82% and 59%. At 82% sensitivity, dermatologist specificity was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating characteristic area of the top fusion algorithm was greater than the mean receiver operating characteristic area of dermatologists (0.86 vs. 0.71, P = .001).
The dataset lacked the full spectrum of skin lesions encountered in clinical practice, particularly banal lesions. Readers and algorithms were not provided clinical data (eg, age or lesion history/symptoms). Results obtained using our study design cannot be extrapolated to clinical practice.
Deep learning computer vision systems classified melanoma dermoscopy images with accuracy that exceeded some but not all dermatologists
Evaluación automática de la vista crítica de seguridad utilizando deep learning en la base de datos Cholec80-CVS
No full textLa colecistectomía laparoscópica es un procedimiento quirúrgico mínimamente invasivo utilizado
para la extirpación de la vesícula biliar que puede provocar lesiones en el conducto biliar. Para
prevenir estas lesiones, Strasberg y sus colegas propusieron el método Critical View of Safety para
identificar el conducto cístico y la arteria durante estos procedimientos. En este trabajo entrenamos
modelos de aprendizaje profundo con el primer conjunto de datos de código abierto de vídeos
de colistectomía laparoscópica que contienen anotaciones de los criterios de Strasberg, llamado
Cholec80-CVS. Este estudio representa el primer intento de investigar el desempeño de los modelos de
aprendizaje profundo para ayudar a identificar la vista crítica de seguridad durante los procedimientos
de colecistectomía laparoscópica utilizando el conjunto de datos Cholec80-CVS, y proporciona
información sobre las limitaciones y los enfoques potenciales para futuras investigaciones en esta
áreaIngeniero ElectrónicoPregradoDeep learningMachine learningInteligencia artificialComputer visio
