Improved correlation of histological data with DCE MRI parameter maps by 3D reconstruction, reslicing and parameterization of the histological images

Abstract Although gold-standard histological assessment is subjective it remains central to diagnosis and clinical trial protocols and is crucial for the evaluation of any preclinical disease model. Objectivity and reproducibility are enhanced by quantitative analysis of histological images but current methods require application-specific algorithm training and fail to extract understanding from the histological context of observable features. We reinterpret histopathological images as disease landscapes to describe a generalisable framework defining topographic relationships in tissue using geoscience approaches. The framework requires no user-dependent training to operate on all image datasets in a classifier-agnostic manner but is adaptable and scalable, able to quantify occult abnormalities, derive mechanistic insights, and define a new feature class for machine-learning diagnostic classification. We demonstrate application to inflammatory, fibrotic and neoplastic disease in multiple organs, including the detection and quantification of occult lobular enlargement in the liver secondary to hilar obstruction. We anticipate this approach will provide a robust class of histological data for trial stratification or endpoints, provide quantitative endorsement of experimental models of disease, and could be incorporated within advanced approaches to clinical diagnostic pathology.

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Stimulated Raman histology for rapid and accurate intraoperative diagnosis of CNS tumors: prospective blinded study

Journal of Neurosurgery ◽

10.3171/2019.9.jns192075 ◽

2019 ◽

pp. 1-7

Author(s):

Daniel G. Eichberg ◽

Ashish H. Shah ◽

Long Di ◽

Alexa M. Semonche ◽

George Jimsheleishvili ◽

...

Keyword(s):

Frozen Section ◽

Cns Tumors ◽

Rapid Acquisition ◽

Novel Technology ◽

Permanent Section ◽

Diagnostic Time ◽

Blinded Study ◽

Histological Data ◽

Histological Images ◽

Stimulated Raman

OBJECTIVEIn some centers where brain tumor surgery is performed, the opportunity for expert intraoperative neuropathology consultation is lacking. Consequently, surgeons may not have access to the highest quality diagnostic histological data to inform surgical decision-making. Stimulated Raman histology (SRH) is a novel technology that allows for rapid acquisition of diagnostic histological images at the bedside.METHODSThe authors performed a prospective blinded cohort study of 82 consecutive patients undergoing resection of CNS tumors to compare diagnostic time and accuracy of SRH simulation to the gold standard, i.e., frozen and permanent section diagnosis. Diagnostic accuracy was determined by concordance of SRH-simulated intraoperative pathology consultation with a blinded board-certified neuropathologist, with official frozen section and permanent section results.RESULTSOverall, the mean time to diagnosis was 30.5 ± 13.2 minutes faster (p < 0.0001) for SRH simulation than for frozen section, with similar diagnostic correlation: 91.5% (κ = 0.834, p < 0.0001) between SRH simulation and permanent section, and 91.5% between frozen and permanent section (κ = 0.894, p < 0.0001).CONCLUSIONSSRH-simulated intraoperative pathology consultation was significantly faster and equally accurate as frozen section.

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3D Reconstruction of dental specimens from 2D histological images and μCT-Scans

Computer Methods in Biomechanics & Biomedical Engineering ◽

10.1080/10255840500296140 ◽

2005 ◽

Vol 8 (3) ◽

pp. 167-176 ◽

Cited By ~ 23

Author(s):

A. Rahimi ◽

L. Keilig ◽

G. Bendels ◽

R. Klein ◽

T.M. Buzug ◽

...

Keyword(s):

3D Reconstruction ◽

Histological Images

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A deep matrix completion method for imputing missing histological data in breast cancer by integrating DCE‐MRI radiomics

Medical Physics ◽

10.1002/mp.15316 ◽

2021 ◽

Author(s):

Ming Fan ◽

You Zhang ◽

Zhenyu Fu ◽

Maosheng Xu ◽

Shiwei Wang ◽

...

Keyword(s):

Breast Cancer ◽

Matrix Completion ◽

Dce Mri ◽

Histological Data

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Slice-to-Volume Nonrigid Registration of Histological Sections to MR Images of the Human Brain

Anatomy Research International ◽

10.1155/2011/287860 ◽

2011 ◽

Vol 2011 ◽

pp. 1-17 ◽

Cited By ~ 18

Author(s):

Sergey Osechinskiy ◽

Frithjof Kruggel

Keyword(s):

Human Brain ◽

Three Dimensional ◽

Magnetic Resonance Images ◽

Geometric Transformation ◽

Nonrigid Registration ◽

Mr Images ◽

B Splines ◽

Histological Data ◽

Histological Images ◽

Flexible Framework

Registration of histological images to three-dimensional imaging modalities is an important step in quantitative analysis of brain structure, in architectonic mapping of the brain, and in investigation of the pathology of a brain disease. Reconstruction of histology volume from serial sections is a well-established procedure, but it does not address registration of individual slices from sparse sections, which is the aim of the slice-to-volume approach. This study presents a flexible framework for intensity-based slice-to-volume nonrigid registration algorithms with a geometric transformation deformation field parametrized by various classes of spline functions: thin-plate splines (TPS), Gaussian elastic body splines (GEBS), or cubic B-splines. Algorithms are applied to cross-modality registration of histological and magnetic resonance images of the human brain. Registration performance is evaluated across a range of optimization algorithms and intensity-based cost functions. For a particular case of histological data, best results are obtained with a TPS three-dimensional (3D) warp, a new unconstrained optimization algorithm (NEWUOA), and a correlation-coefficient-based cost function.

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Method for in vivo assessment of cancer tissue inhomogeneity and accurate histology-like morphological segmentation based on Optical Coherence Elastography

10.1101/2020.02.06.937417 ◽

2020 ◽

Author(s):

Anton A. Plekhanov ◽

Marina A. Sirotkina ◽

Alexander A. Sovetsky ◽

Ekaterina V. Gubarkova ◽

Sergey S. Kuznetsov ◽

...

Keyword(s):

Tumor Development ◽

High Sensitivity ◽

Cancer Tissue ◽

Optical Coherence ◽

Morphological Alterations ◽

Morphological Segmentation ◽

Histological Data ◽

Stiffness Map ◽

Histological Images

AbstractWe present a non-invasive method based on Optical Coherence Elastography (OCE) enabling the in vivo segmentation of morphological tissue constituents, in particular, monitoring of morphological alterations during both tumor development and its response to therapies. The method uses compressional OCE to reconstruct tissue stiffness map as the first step. Then the OCE-image is divided into regions, for which the Young’s modulus (stiffness) falls in specific ranges corresponding to the morphological constituents to be discriminated. These stiffness ranges (characteristic “stiffness spectra”) are initially determined by careful comparison of the “gold-standard” histological data and the OCE-based stiffness map for the corresponding tissue regions. After such precalibration, the results of morphological segmentation of OCE-images demonstrate a striking correlation with the histological results in terms of percentage of the segmented zones. To demonstrate high sensitivity of the OCE-method and its excellent correlation with conventional histological segmentation we present results obtained in vivo on a murine model of breast cancer in comparative experimental study of the efficacy of two anti-tumor chemotherapeutic drugs with different mechanisms of action. The new technique allowed in vivo monitoring and quantitative segmentation of (i) viable, (ii) dystrophic, (iii) necrotic tumor cells and (iv) edema zones very similar to morphological segmentation of histological images. Numerous applications in other experimental/clinical areas requiring rapid, nearly real-time, quantitative assessment of tissue structure can be foreseen.

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