Colorization, the task of coloring a grayscale image or video, involves assigning from the single dimension of intensity or luminance a quantity that varies in three dimensions, such as red, green, and blue channels. Mapping between intensity and color is, therefore, not unique, and colorization is ambiguous in nature and requires some amount of human interaction or external information. A computationally simple, yet effective, approach of colorization is presented in this paper. The method is fast and it can be conveniently used "on the fly," permitting the user to interactively get the desired results promptly after providing a reduced set of chrominance scribbles. Based on the concepts of luminance-weighted chrominance blending and fast intrinsic distance computations, high-quality colorization results for still images and video are obtained at a fraction of the complexity and computational cost of previously reported techniques. Possible extensions of the algorithm introduced here included the capability of changing the colors of an existing color image or video, as well as changing the underlying luminance, and many other special effects demonstrated here.
In this note we present an implementation of the fast marching algorithm for solving Eikonal equations that in practice reduces the original run-time from O(N log N) to linear. This lower run-time cost is obtained while keeping an error bound of the same order of magnitude as the original algorithm. This improvement is achieved introducing the straight forward untidy priority queue, obtained via a quantization of the priorities in the marching computation. We present the underlying framework, estimations on the error, and examples showing the usefulness of the proposed approach.
IMPORTANCE Expert-level artificial intelligence (AI) algorithms for prostate biopsy grading have recently been developed. However, the potential impact of integrating such algorithms into pathologist workflows remains largely unexplored. OBJECTIVETo evaluate an expert-level AI-based assistive tool when used by pathologists for the grading of prostate biopsies. DESIGN, SETTING, AND PARTICIPANTSThis diagnostic study used a fully crossed multiple-reader, multiple-case design to evaluate an AI-based assistive tool for prostate biopsy grading. Retrospective grading of prostate core needle biopsies from 2 independent medical laboratories in the US was performed between October 2019 and January 2020. A total of 20 general pathologists reviewed 240 prostate core needle biopsies from 240 patients. Each pathologist was randomized to 1 of 2 study cohorts. The 2 cohorts reviewed every case in the opposite modality (with AI assistance vs without AI assistance) to each other, with the modality switching after every 10 cases. After a minimum 4-week washout period for each batch, the pathologists reviewed the cases for a second time using the opposite modality. The pathologist-provided grade group for each biopsy was compared with the majority opinion of urologic pathology subspecialists. EXPOSURE An AI-based assistive tool for Gleason grading of prostate biopsies. MAIN OUTCOMES AND MEASURES Agreement between pathologists and subspecialists with andwithout the use of an AI-based assistive tool for the grading of all prostate biopsies and Gleason grade group 1 biopsies. RESULTSBiopsies from 240 patients (median age, 67 years; range, 39-91 years) with a median prostate-specific antigen level of 6.5 ng/mL (range, 0.6-97.0 ng/mL) were included in the analyses.Artificial intelligence-assisted review by pathologists was associated with a 5.6% increase (95% CI, 3.2%-7.9%; P < .001) in agreement with subspecialists (from 69.7% for unassisted reviews to 75.3% for assisted reviews) across all biopsies and a 6.2% increase (95% CI, 2.7%-9.8%; P = .001) in agreement with subspecialists (from 72.3% for unassisted reviews to 78.5% for assisted reviews) for grade group 1 biopsies. A secondary analysis indicated that AI assistance was also associated with improvements in tumor detection, mean review time, mean self-reported confidence, and interpathologist agreement. CONCLUSIONS AND RELEVANCEIn this study, the use of an AI-based assistive tool for the review of prostate biopsies was associated with improvements in the quality, efficiency, and consistency of cancer detection and grading.
Abstract. We propose a novel method to detect the current state of the quasi-periodic system from image sequences which in turn will enable us to synchronize/gate the image sequences to obtain images of the organ system at similar configurations. The method uses the cumulated phase shift in the spectral domain of successive image frames as a measure of the net motion of objects in the scene. The proposed method is applicable to 2D and 3D time varying sequences and is not specific to the imaging modality. We demonstrate its effectiveness on X-Ray Angiographic and Cardiac and Liver Ultrasound sequences. Knowledge of the current (cardiac or respiratory) phase of the system, opens up the possibility for a purely image based cardiac and respiratory gating scheme for interventional and radiotherapy procedures.
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