Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation

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 base...

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Autores principales:	Lin Li, Shivani Pahwa, Gregory Penzias, Mirabela Rusu, Jay Gollamudi, Satish Viswanath, Anant Madabhushi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/25abe5deed62450c9d569b039a493217
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spelling	oai:doaj.org-article:25abe5deed62450c9d569b039a4932172021-12-02T12:32:19ZCo-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation10.1038/s41598-017-08969-w2045-2322https://doaj.org/article/25abe5deed62450c9d569b039a4932172017-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-08969-whttps://doaj.org/toc/2045-2322Abstract 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.Lin LiShivani PahwaGregory PenziasMirabela RusuJay GollamudiSatish ViswanathAnant MadabhushiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Lin Li Shivani Pahwa Gregory Penzias Mirabela Rusu Jay Gollamudi Satish Viswanath Anant Madabhushi Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation
description	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.
format	article
author	Lin Li Shivani Pahwa Gregory Penzias Mirabela Rusu Jay Gollamudi Satish Viswanath Anant Madabhushi
author_facet	Lin Li Shivani Pahwa Gregory Penzias Mirabela Rusu Jay Gollamudi Satish Viswanath Anant Madabhushi
author_sort	Lin Li
title	Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation
title_short	Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation
title_full	Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation
title_fullStr	Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation
title_full_unstemmed	Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation
title_sort	co-registration of ex vivo surgical histopathology and in vivo t2 weighted mri of the prostate via multi-scale spectral embedding representation
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/25abe5deed62450c9d569b039a493217
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Co-Registration of ex vivo Surgical Histopathology and in vivo T2 weighted MRI of the Prostate via multi-scale spectral embedding representation

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