A
Assaf Hoogi
Researcher at Stanford University
Publications - 36
Citations - 2018
Assaf Hoogi is an academic researcher from Stanford University. The author has contributed to research in topics: Segmentation & Active contour model. The author has an hindex of 15, co-authored 34 publications receiving 1378 citations. Previous affiliations of Assaf Hoogi include Erasmus University Rotterdam & Technion – Israel Institute of Technology.
Papers
More filters
Journal ArticleDOI
Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions
TL;DR: An overview of current deep learning-based segmentation approaches for quantitative brain MRI is provided and a critical assessment of the current state and likely future developments and trends is provided.
Journal ArticleDOI
A curated mammography data set for use in computer-aided detection and diagnosis research.
TL;DR: The data set, the CBIS-DDSM (Curated Breast Imaging Subset of DDSM), includes decompressed images, data selection and curation by trained mammographers, updated mass segmentation and bounding boxes, and pathologic diagnosis for training data, formatted similarly to modern computer vision data sets.
Journal ArticleDOI
Automated classification of brain tumor type in whole-slide digital pathology images using local representative tiles.
TL;DR: The proposed method uses a coarse-to-fine analysis of the localized characteristics in pathology images to automatically differentiate between the two cancer subtypes and showed high stability and robustness to parameter variation.
Journal ArticleDOI
Carotid Plaque Vulnerability: Quantification of Neovascularization on Contrast-Enhanced Ultrasound With Histopathologic Correlation
TL;DR: The newly developed method allowed quantification of the intraplaque neovascularization as a feature of vulnerability in the carotid plaque and proved to be highly correlated with histopathologic results.
Journal ArticleDOI
Adaptive local window for level set segmentation of CT and MRI liver lesions
Assaf Hoogi,Christopher F. Beaulieu,Guilherme Moura Cunha,Elhamy Heba,Claude B. Sirlin,Sandy Napel,Daniel L. Rubin +6 more
TL;DR: A novel method to estimate the adaptive local window size surrounding each contour point, which outperforms the state of the art methods in terms of agreement with the manual marking and segmentation robustness to contour initialization or the energy model used.