Literature DB >> 21255974

Determining histology-MRI slice correspondences for defining MRI-based disease signatures of prostate cancer.

Gaoyu Xiao1, B Nicolas Bloch, Jonathan Chappelow, Elizabeth M Genega, Neil M Rofsky, Robert E Lenkinski, John Tomaszewski, Michael D Feldman, Mark Rosen, Anant Madabhushi.   

Abstract

Mapping the spatial disease extent in a certain anatomical organ/tissue from histology images to radiological images is important in defining the disease signature in the radiological images. One such scenario is in the context of men with prostate cancer who have had pre-operative magnetic resonance imaging (MRI) before radical prostatectomy. For these cases, the prostate cancer extent from ex vivo whole-mount histology is to be mapped to in vivo MRI. The need for determining radiology-image-based disease signatures is important for (a) training radiologist residents and (b) for constructing an MRI-based computer aided diagnosis (CAD) system for disease detection in vivo. However, a prerequisite for this data mapping is the determination of slice correspondences (i.e. indices of each pair of corresponding image slices) between histological and magnetic resonance images. The explicit determination of such slice correspondences is especially indispensable when an accurate 3D reconstruction of the histological volume cannot be achieved because of (a) the limited tissue slices with unknown inter-slice spacing, and (b) obvious histological image artifacts (tissue loss or distortion). In the clinic practice, the histology-MRI slice correspondences are often determined visually by experienced radiologists and pathologists working in unison, but this procedure is laborious and time-consuming. We present an iterative method to automatically determine slice correspondence between images from histology and MRI via a group-wise comparison scheme, followed by 2D and 3D registration. The image slice correspondences obtained using our method were compared with the ground truth correspondences determined via consensus of multiple experts over a total of 23 patient studies. In most instances, the results of our method were very close to the results obtained via visual inspection by these experts.
Copyright © 2010 Elsevier Ltd. All rights reserved.

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Year:  2011        PMID: 21255974     DOI: 10.1016/j.compmedimag.2010.12.003

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


  25 in total

1.  Computer-aided diagnosis of prostate cancer with MRI.

Authors:  Baowei Fei
Journal:  Curr Opin Biomed Eng       Date:  2017-09

2.  Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data.

Authors:  Walid M Abdelmoula; Michael S Regan; Begona G C Lopez; Elizabeth C Randall; Sean Lawler; Ann C Mladek; Michal O Nowicki; Bianca M Marin; Jeffrey N Agar; Kristin R Swanson; Tina Kapur; Jann N Sarkaria; William Wells; Nathalie Y R Agar
Journal:  Anal Chem       Date:  2019-04-22       Impact factor: 6.986

3.  Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information.

Authors:  Jonathan Chappelow; B Nicolas Bloch; Neil Rofsky; Elizabeth Genega; Robert Lenkinski; William DeWolf; Anant Madabhushi
Journal:  Med Phys       Date:  2011-04       Impact factor: 4.071

4.  Prostatome: a combined anatomical and disease based MRI atlas of the prostate.

Authors:  Mirabela Rusu; B Nicolas Bloch; Carl C Jaffe; Elizabeth M Genega; Robert E Lenkinski; Neil M Rofsky; Ernest Feleppa; Anant Madabhushi
Journal:  Med Phys       Date:  2014-07       Impact factor: 4.071

5.  Optimized SIFTFlow for registration of whole-mount histology to reference optical images.

Authors:  Rushin Shojaii; Anne L Martel
Journal:  J Med Imaging (Bellingham)       Date:  2016-10-19

6.  Imaging of prostate cancer: a platform for 3D co-registration of in-vivo MRI ex-vivo MRI and pathology.

Authors:  Clement Orczyk; Artem Mikheev; Andrew B Rosenkrantz; Jonathan Melamed; Samir S Taneja; Henry Rusinek
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2012-02-23

7.  Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS.

Authors:  Pallavi Tiwari; John Kurhanewicz; Anant Madabhushi
Journal:  Med Image Anal       Date:  2012-12-13       Impact factor: 8.545

8.  Identifying in vivo DCE MRI markers associated with microvessel architecture and gleason grades of prostate cancer.

Authors:  Asha Singanamalli; Mirabela Rusu; Rachel E Sparks; Natalie N C Shih; Amy Ziober; Li-Ping Wang; John Tomaszewski; Mark Rosen; Michael Feldman; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2015-06-25       Impact factor: 4.813

9.  Novel PCA-VIP scheme for ranking MRI protocols and identifying computer-extracted MRI measurements associated with central gland and peripheral zone prostate tumors.

Authors:  Shoshana B Ginsburg; Satish E Viswanath; B Nicolas Bloch; Neil M Rofsky; Elizabeth M Genega; Robert E Lenkinski; Anant Madabhushi
Journal:  J Magn Reson Imaging       Date:  2014-06-18       Impact factor: 4.813

10.  Histology-derived volumetric annotation of the human hippocampal subfields in postmortem MRI.

Authors:  Daniel H Adler; John Pluta; Salmon Kadivar; Caryne Craige; James C Gee; Brian B Avants; Paul A Yushkevich
Journal:  Neuroimage       Date:  2013-09-12       Impact factor: 6.556

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