ArticleMedical physics2020
Registration of presurgical MRI and histopathology images from radical prostatectomy via RAPSODI.
Article in Medical physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Algorithms on the rise: a machine learning-driven survey of prostate cancer literature.Frontiers in oncology · 2025Pooled it
- Expanded methods for geometric location of 2D bone histology co-registration in 3D radiology volumes.Physical and engineering sciences in medicine · 2026Article
- Enhancing foundation model transfer for prostate cancer detection with patch-level contrastive learning.NPJ digital medicine · 2026Article
- Three-dimensional reconstruction of gigapixel whole-mount histopathology specimens with RAPID.Scientific reports · 2026Article
- Post hoc estimation of a quantitative restriction spectrum imaging biomarker for prostate cancer detection using conventional MRI.Journal of applied clinical medical physics · 2026Article
- A Proof-of-Concept Solution for Co-locating 2D Histology Images in 3D for Histology-to-CT and MR Image Registration: Closing the Loop for Bone Sarcoma Treatment Planning.Journal of imaging informatics in medicine · 2025Article
- Supporting intraoperative margin assessment using deep learning for automatic tumour segmentation in breast lumpectomy micro-PET-CT.NPJ breast cancer · 2025Article
- Deep Learning-based Anatomy-Aware Morph Model for Registration of Prostate Whole-Mount Histopathology to MRI.Radiology. Imaging cancer · 2025Article
- Development and validation of a framework for registration of whole-mount radical prostatectomy histopathology with three-dimensional transrectal ultrasound.BMC urology · 2025Article
- ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies.European urology oncology · 2025Article
- ProstAtlasDiff: Prostate cancer detection on MRI using Diffusion Probabilistic Models guided by population spatial cancer atlases.Medical image analysis · 2025Article
- Spatial integration of multi-omics data from serial sections using the novel Multi-Omics Imaging Integration Toolset.GigaScience · 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
- 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
- Full resolution reconstruction of whole-mount sections from digitized individual tissue fragments.Scientific reports · 2024Article
- [Bioengineering (Basel, Switzerland) · 2023Article
- Prediction and Mapping of Intraprostatic Tumor Extent with Artificial Intelligence.European urology open science · 2023Article
- Application of Swarm Intelligence Optimization Algorithms in Image Processing: A Comprehensive Review of Analysis, Synthesis, and Optimization.Biomimetics (Basel, Switzerland) · 2023Review
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Authors and funding
12 authors.
Funding
Abstract
purposeMagnetic resonance imaging (MRI) has great potential to improve prostate cancer diagnosis; however, subtle differences between cancer and confounding conditions render prostate MRI interpretation challenging. The tissue collected from patients who undergo radical prostatectomy provides a unique opportunity to correlate histopathology images of the prostate with preoperative MRI to accurately map the extent of cancer from histopathology images onto MRI. We seek to develop an open-source, easy-to-use platform to align presurgical MRI and histopathology images of resected prostates in patients who underwent radical prostatectomy to create accurate cancer labels on MRI.
methodsHere, we introduce RAdiology Pathology Spatial Open-Source multi-Dimensional Integration (RAPSODI), the first open-source framework for the registration of radiology and pathology images. RAPSODI relies on three steps. First, it creates a three-dimensional (3D) reconstruction of the histopathology specimen as a digital representation of the tissue before gross sectioning. Second, RAPSODI registers corresponding histopathology and MRI slices. Third, the optimized transforms are applied to the cancer regions outlined on the histopathology images to project those labels onto the preoperative MRI.
resultsWe tested RAPSODI in a phantom study where we simulated various conditions, for example, tissue shrinkage during fixation. Our experiments showed that RAPSODI can reliably correct multiple artifacts. We also evaluated RAPSODI in 157 patients from three institutions that underwent radical prostatectomy and have very different pathology processing and scanning. RAPSODI was evaluated in 907 corresponding histpathology-MRI slices and achieved a Dice coefficient of 0.97 ± 0.01 for the prostate, a Hausdorff distance of 1.99 ± 0.70 mm for the prostate boundary, a urethra deviation of 3.09 ± 1.45 mm, and a landmark deviation of 2.80 ± 0.59 mm between registered histopathology images and MRI.
conclusionOur robust framework successfully mapped the extent of cancer from histopathology slices onto MRI providing labels from training machine learning methods to detect cancer on MRI.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.