Co-registration of Histological, Optical and MR Data of the Human Brain

Bardinet, Éric; Ourselin, Sébastien; Dormont, Didier; Malandain, Grégoire; Tandé, Dominique; Parain, Karine; Ayache, Nicholas; Yelnik, Jérôme

doi:10.1007/3-540-45786-0_68

Cited by 40 publications

(37 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Performing voxel-based registration allows for spatially local comparison of MRI and histology, and the scale of this analysis is dependent on the achievable registration accuracy. Many previous studies in MRI and histology registration (see Table 3 2004; Meyer et al, 2006;Lebenberg et al, 2010;Schormann et al, 1995;Yelnik et al, 2007;Osechinskiy and Kruggel, 2011;Bardinet et al, 2002), and furthermore many previous studies included evaluation on only one dataset (Malandain et al, 2004;Choe et al, 2011;Meyer et al, 2006;Schormann et al, 1995;Kim et al, 2000;Yelnik et al, 2007;Osechinskiy and Kruggel, 2011;Bardinet et al, 2002;Lazebnik et al, 2003). Of the studies that did report accuracy on more than one dataset, TRE ranged from sub-millimeter (Ceritoglu et al, 2010;Jacobs et al, 1999;Yang et al, 2012) to 3-5 mm (Liu et al, 2012;Singh et al, 2008).…”

Section: Discussionmentioning

confidence: 99%

Registration of in-vivo to ex-vivo MRI of surgically resected specimens: A pipeline for histology to in-vivo registration

Goubran

Ribaupierre

Hammond

et al. 2015

Journal of Neuroscience Methods

View full text Add to dashboard Cite

h i g h l i g h t s• We present a protocol for registration of in-vivo to ex-vivo brain specimens.• This protocol completes a registration pipeline for histology to in-vivo MRI.• A TRE of 1.35 ± 0.11 mm (neocortex) and 1.41 ± 0.33 mm (hippocampus) was found.• Deformable registration significantly improved the registration accuracy.• This pipeline allows for the assessment of pathological correlates in MRI. a r t i c l e i n f o t r a c tBackground: Advances in MRI have the potential to improve surgical treatment of epilepsy through improved identification and delineation of lesions. However, validation is currently needed to investigate histopathological correlates of these new imaging techniques. The purpose of this work is to develop and evaluate a protocol for deformable image registration of in-vivo to ex-vivo resected brain specimen MRI. This protocol, in conjunction with our previous work on ex-vivo to histology registration, completes a registration pipeline for histology to in-vivo MRI, enabling voxel-based validation of novel and existing MRI techniques with histopathology. New method: A combination of image-based and landmark-based 3D registration was used to register in-vivo MRI and the ex-vivo MRI from patients (N = 10) undergoing epilepsy surgery. Target registration error (TRE) was used to assess accuracy and the added benefit of deformable registration. Results: A mean TRE of 1.35 ± 0.11 and 1.41 ± 0.33 mm was found for neocortical and hippocampal specimens respectively. Statistical analysis confirmed that the deformable registration significantly improved the registration accuracy for both specimens.Comparison with existing methods: Image registration of surgically resected brain specimens is a unique application which presents numerous technical challenges and that have not been fully addressed in previous literature. Our computed TRE are comparable to previous attempts tackling similar applications, as registering in-vivo MRI to whole brain or serial histology. Conclusion: The presented registration pipeline finds dense and accurate spatial correspondence between in-vivo MRI and histology and allows for the spatially local and quantitative assessment of pathological correlates in MRI.

show abstract

Section: Discussionmentioning

confidence: 99%

Registration of in-vivo to ex-vivo MRI of surgically resected specimens: A pipeline for histology to in-vivo registration

Goubran

Ribaupierre

Hammond

et al. 2015

Journal of Neuroscience Methods

View full text Add to dashboard Cite

show abstract

“…3-D in-vivo imaging or 2-D block-face images acquired during histological sectioning [2,6,5]. A comprehensive overview of recent techniques is given by Cifor et al [4].…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of 3-D Histology Images by Simultaneous Deformable Registration

Feuerstein

Heibel

Gardiazabal

et al. 2011

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. The reconstruction of histology sections into a 3-D volume receives increased attention due to its various applications in modern medical image analysis. To guarantee a geometrically coherent reconstruction, we propose a new way to register histological sections simultaneously to previously acquired reference images and to neighboring slices in the stack. To this end, we formulate two potential functions and associate them to the same Markov random field through which we can efficiently find an optimal solution. Due to our simultaneous formulation and the absence of any segmentation step during the reconstruction we can dramatically reduce error propagation effects. This is illustrated by experiments on carefully created synthetic as well as real data sets.

show abstract

“…Pioneer work [5,6] used affine registration to align histology-MRI of the brain and presented promising results, but affine transformation has limited ability to deal with the non-linear distortions. To deal with nonlinear distortions, Pitiot et al [7] automatically partitioned images into pieces and used piece-wise affine model with the final integrated deformable deformations.…”

Section: Introductionmentioning

confidence: 99%

Non-rigid registration between histological and MR images of the prostate: A joint segmentation and registration framework

Shen

Feldman

et al. 2009

2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

View full text Add to dashboard Cite

This paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region overlap between the two images. The former aims to capture distortions at prostate boundary and internal bloblike structures; and the latter aims to capture distortions specifically at cancer regions. In particular, landmark similarities, the former, is maximized by an annealing process, where correspondences between the automatically-detected boundary and internal landmarks are iteratively established in a fuzzy-to-deterministic fashion. Cancer region overlap, the latter, is maximized in a joint cancer segmentation and registration framework, where the two interleaved problems -segmentation and registration -inform each other in an iterative fashion. Registration accuracy is established by comparing against human-rater-defined landmarks and by comparing with other methods. The ultimate goal of this registration is to warp the histologically-defined cancer ground truth into MRI, for more thoroughly understanding MRI signal characteristics of the prostate cancerous tissue, which will promote the MRI-based prostate cancer diagnosis in the future studies.

show abstract

Co-registration of Histological, Optical and MR Data of the Human Brain

Cited by 40 publications

References 10 publications

Registration of in-vivo to ex-vivo MRI of surgically resected specimens: A pipeline for histology to in-vivo registration

Registration of in-vivo to ex-vivo MRI of surgically resected specimens: A pipeline for histology to in-vivo registration

Reconstruction of 3-D Histology Images by Simultaneous Deformable Registration

Non-rigid registration between histological and MR images of the prostate: A joint segmentation and registration framework

Contact Info

Product

Resources

About