Literature DB >> 29276002

Intensity-based volumetric registration of magnetic resonance images and whole-mount sections of the prostate.

Are Losnegård1, Lars Reisæter2, Ole J Halvorsen3, Christian Beisland4, Aurea Castilho5, Ludvig P Muren6, Jarle Rørvik7, Arvid Lundervold8.   

Abstract

OBJECTIVE: Magnetic Resonance Imaging (MRI) of the prostate provides useful in vivo diagnostic tissue information such as tumor location and aggressiveness, but ex vivo histopathology remains the ground truth. There are several challenges related to the registration of MRI to histopathology. We present a method for registration of standard clinical T2-weighted MRI (T2W-MRI) and transverse histopathology whole-mount (WM) sections of the prostate.
METHODS: An isotropic volume stack was created from the WM sections using 2D rigid and deformable registration combined with linear interpolation. The prostate was segmented manually from the T2W-MRI volume and registered to the WM section volume using a combination of affine and deformable registration. The method was evaluated on a set of 12 patients who had undergone radical prostatectomy. Registration accuracy was assessed using volume overlap (Dice Coefficient, DC) and landmark distances.
RESULTS: The DC was 0.94 for the whole prostate, 0.63 for the peripheral zone and 0.77 for the remaining gland. The landmark distances were on average 5.4 mm.
CONCLUSION: The volume overlap for the whole prostate and remaining gland, as well as the landmark distances indicate good registration accuracy for the proposed method, and shows that it can be highly useful for registering clinical available MRI and WM sections of the prostate.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Image registration; MR imaging; Prostate cancer; Whole-mount sections

Mesh:

Year:  2017        PMID: 29276002     DOI: 10.1016/j.compmedimag.2017.12.002

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  10 in total

1.  Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Authors:  Indrani Bhattacharya; Arun Seetharaman; Christian Kunder; Wei Shao; Leo C Chen; Simon J C Soerensen; Jeffrey B Wang; Nikola C Teslovich; Richard E Fan; Pejman Ghanouni; James D Brooks; Geoffrey A Sonn; Mirabela Rusu
Journal:  Med Image Anal       Date:  2021-11-06       Impact factor: 8.545

2.  3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction.

Authors:  Rewa R Sood; Wei Shao; Christian Kunder; Nikola C Teslovich; Jeffrey B Wang; Simon J C Soerensen; Nikhil Madhuripan; Anugayathri Jawahar; James D Brooks; Pejman Ghanouni; Richard E Fan; Geoffrey A Sonn; Mirabela Rusu
Journal:  Med Image Anal       Date:  2021-01-23       Impact factor: 8.545

3.  Registration of presurgical MRI and histopathology images from radical prostatectomy via RAPSODI.

Authors:  Mirabela Rusu; Wei Shao; Christian A Kunder; Jeffrey B Wang; Simon J C Soerensen; Nikola C Teslovich; Rewa R Sood; Leo C Chen; Richard E Fan; Pejman Ghanouni; James D Brooks; Geoffrey A Sonn
Journal:  Med Phys       Date:  2020-07-18       Impact factor: 4.071

4.  ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.

Authors:  Wei Shao; Linda Banh; Christian A Kunder; Richard E Fan; Simon J C Soerensen; Jeffrey B Wang; Nikola C Teslovich; Nikhil Madhuripan; Anugayathri Jawahar; Pejman Ghanouni; James D Brooks; Geoffrey A Sonn; Mirabela Rusu
Journal:  Med Image Anal       Date:  2020-12-17       Impact factor: 8.545

5.  A deep learning based framework for the registration of three dimensional multi-modal medical images of the head.

Authors:  Kh Tohidul Islam; Sudanthi Wijewickrema; Stephen O'Leary
Journal:  Sci Rep       Date:  2021-01-21       Impact factor: 4.379

6.  The impact of the co-registration technique and analysis methodology in comparison studies between advanced imaging modalities and whole-mount-histology reference in primary prostate cancer.

Authors:  Constantinos Zamboglou; Maria Kramer; Selina Kiefer; Peter Bronsert; Lara Ceci; August Sigle; Wolfgang Schultze-Seemann; Cordula A Jilg; Tanja Sprave; Thomas F Fassbender; Nils H Nicolay; Juri Ruf; Matthias Benndorf; Anca L Grosu; Simon K B Spohn
Journal:  Sci Rep       Date:  2021-03-12       Impact factor: 4.379

7.  Histology to 3D in vivo MR registration for volumetric evaluation of MRgFUS treatment assessment biomarkers.

Authors:  Blake E Zimmerman; Sara L Johnson; Henrik A Odéen; Jill E Shea; Rachel E Factor; Sarang C Joshi; Allison H Payne
Journal:  Sci Rep       Date:  2021-09-23       Impact factor: 4.379

8.  Bridging the gap between prostate radiology and pathology through machine learning.

Authors:  Indrani Bhattacharya; David S Lim; Han Lin Aung; Xingchen Liu; Arun Seetharaman; Christian A Kunder; Wei Shao; Simon J C Soerensen; Richard E Fan; Pejman Ghanouni; Katherine J To'o; James D Brooks; Geoffrey A Sonn; Mirabela Rusu
Journal:  Med Phys       Date:  2022-06-13       Impact factor: 4.506

Review 9.  A review of artificial intelligence in prostate cancer detection on imaging.

Authors:  Indrani Bhattacharya; Yash S Khandwala; Sulaiman Vesal; Wei Shao; Qianye Yang; Simon J C Soerensen; Richard E Fan; Pejman Ghanouni; Christian A Kunder; James D Brooks; Yipeng Hu; Mirabela Rusu; Geoffrey A Sonn
Journal:  Ther Adv Urol       Date:  2022-10-10

10.  Registration of histopathology to magnetic resonance imaging of prostate cancer.

Authors:  Kristina Sandgren; Erik Nilsson; Angsana Keeratijarut Lindberg; Sara Strandberg; Lennart Blomqvist; Anders Bergh; Bengt Friedrich; Jan Axelsson; Margareta Ögren; Mattias Ögren; Anders Widmark; Camilla Thellenberg Karlsson; Karin Söderkvist; Katrine Riklund; Joakim Jonsson; Tufve Nyholm
Journal:  Phys Imaging Radiat Oncol       Date:  2021-04-12
  10 in total

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