ArticleScientific reports2017
Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
19 citing papers in PubMed.
- Improved ground truth annotation by multimodal image registration from 3D ultrasound to histopathology for resected tongue carcinoma.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Article
- External validation of an artificial intelligence model for Gleason grading of prostate cancer on prostatectomy specimens.BJU international · 2025Article
- Design and evaluation of an open-source block face imaging system for 2D histology to magnetic resonance image registration.MethodsX · 2024Article
- Deformable Mapping of Rectal Cancer Whole-Mount Histology with Restaging MRI at Voxel Scale: A Feasibility Study.Radiology. Imaging cancer · 2024Article
- RAPHIA: A deep learning pipeline for the registration of MRI and whole-mount histopathology images of the prostate.Computers in biology and medicine · 2024Article
- Multi-scale statistical deformation based co-registration of prostate MRI and post-surgical whole mount histopathology.Medical physics · 2024Article
- Multiparametric magnetic resonance imaging for radiation therapy response monitoring in soft tissue sarcomas: a histology and MRI co-registration algorithm.Theranostics · 2023Article
- Ten quick tips for computational analysis of medical images.PLoS computational biology · 2023Article
- Novel radiomic analysis on bi-parametric MRI for characterizing differences between MR non-visible and visible clinically significant prostate cancer.European journal of radiology open · 2023Article
- Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.Medical image analysis · 2022Article
- Histology to 3D in vivo MR registration for volumetric evaluation of MRgFUS treatment assessment biomarkers.Scientific reports · 2021Article
- Registration of histopathology to magnetic resonance imaging of prostate cancer.Physics and imaging in radiation oncology · 2021Article
- 3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction.Medical image analysis · 2021Article
- Harnessing non-destructive 3D pathology.Nature biomedical engineering · 2021Review
- T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.European radiology · 2021Article
- ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.Medical image analysis · 2021Article
- An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI.Scientific reports · 2020Article
- Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings.European radiology · 2019Article
- A review of artificial intelligence in prostate cancer detection on imaging.Therapeutic advances in urologyReview
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Authors and funding
7 authors.
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Abstract
Multi-modal image co-registration via optimizing mutual information (MI) is based on the assumption that intensity distributions of multi-modal images follow a consistent relationship. However, images with a substantial difference in appearance violate this assumption, thus MI directly based on image intensity alone may be inadequate to drive similarity based co-registration. To address this issue, we introduce a novel approach for multi-modal co-registration called Multi-scale Spectral Embedding Registration (MSERg). MSERg involves the construction of multi-scale spectral embedding (SE) representations from multimodal images via texture feature extraction, scale selection, independent component analysis (ICA) and SE to create orthogonal representations that decrease the dissimilarity between the fixed and moving images to facilitate better co-registration. To validate the MSERg method, we aligned 45 pairs of in vivo prostate MRI and corresponding ex vivo histopathology images. The dataset was split into a learning set and a testing set. In the learning set, length scales of 5 × 5, 7 × 7 and 17 × 17 were selected. In the independent testing set, we compared MSERg with intensity-based registration, multi-attribute combined mutual information (MACMI) registration and scale-invariant feature transform (SIFT) flow registration. Our results suggest that multi-scale SE representations generated by MSERg are found to be more appropriate for radiology-pathology co-registration.
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