Cathie Crukley

Registration of prostate histology images to ex vivo MR images via strand-shaped fiducials.

To present and evaluate a method for registration of whole‐mount prostate digital histology images to ex vivo magnetic resonance (MR) images.

Prostate: Registration of Digital Histopathologic Images to in Vivo MR Images Acquired by Using Endorectal Receive Coil

PURPOSE To develop and evaluate a technique for the registration of in vivo prostate magnetic resonance (MR) images to digital histopathologic images by using image-guided specimen slicing based on strand-shaped fiducial markers relating specimen imaging to histopathologic examination. MATERIALS AND METHODS The study was approved by the institutional review board (the University of Western Ontario Health Sciences Research Ethics Board, London, Ontario, Canada), and written informed consent was obtained from all patients. This work proposed and evaluated a technique utilizing developed fiducial markers and real-time three-dimensional visualization in support of image guidance for ex vivo prostate specimen slicing parallel to the MR imaging planes prior to digitization, simplifying the registration process. Means, standard deviations, root-mean-square errors, and 95% confidence intervals are reported for all evaluated measurements. RESULTS The slicing error was within the 2.2 mm thickness of the diagnostic-quality MR imaging sections, with a tissue block thickness standard deviation of 0.2 mm. Rigid registration provided negligible postregistration overlap of the smallest clinically important tumors (0.2 cm(3)) at histologic examination and MR imaging, whereas the tested nonrigid registration method yielded a mean target registration error of 1.1 mm and provided useful coregistration of such tumors. CONCLUSION This method for the registration of prostate digital histopathologic images to in vivo MR images acquired by using an endorectal receive coil was sufficiently accurate for coregistering the smallest clinically important lesions with 95% confidence.

Image registration of ex-vivo MRI to sparsely sectioned histology of hippocampal and neocortical temporal lobe specimens.

Intractable or drug-resistant epilepsy occurs in up to 30% of epilepsy patients, with many of these patients undergoing surgical excision of the affected brain region to achieve seizure control. Recent magnetic resonance imaging (MRI) sequences and analysis techniques have the potential to detect abnormalities not identified with diagnostic MRI protocols. Prospective studies involving pre-operative imaging and collection of surgically-resected tissue provide a unique opportunity for verification and tuning of these image analysis techniques, since direct comparison can be made against histopathology, and can lead to better prediction of surgical outcomes and potentially less invasive procedures. To carry out MRI and histology comparison, spatial correspondence between the MR images and the histology images must be found. Towards this goal, a novel pipeline is presented here for bringing ex-vivo MRI of surgically-resected temporal lobe specimens and digital histology into spatial correspondence. The sparsely-sectioned histology images represent a challenge for 3D reconstruction which we address with a combined 3D and 2D registration algorithm that alternates between slice-based and volume-based registration with the ex-vivo MRI. We evaluated our registration method on specimens resected from patients undergoing anterior temporal lobectomy (N=7) and found our method to have a mean target registration error of 0.76±0.66 and 0.98±0.60 mm for hippocampal and neocortical specimens respectively. This work allows for the spatially-local comparison of histology with post-operative MRI and paves the way for eventual correlation with pre-operative MRI image analysis techniques.

3D prostate histology image reconstruction: Quantifying the impact of tissue deformation and histology section location

Background: Guidelines for localizing prostate cancer on imaging are ideally informed by registered post-prostatectomy histology. 3D histology reconstruction methods can support this by reintroducing 3D spatial information lost during histology processing. The need to register small, high-grade foci drives a need for high accuracy. Accurate 3D reconstruction method design is impacted by the answers to the following central questions of this work. (1) How does prostate tissue deform during histology processing? (2) What spatial misalignment of the tissue sections is induced by microtome cutting? (3) How does the choice of reconstruction model affect histology reconstruction accuracy? Materials and Methods: Histology, paraffin block face and magnetic resonance images were acquired for 18 whole mid-gland tissue slices from six prostates. 7-15 homologous landmarks were identified on each image. Tissue deformation due to histology processing was characterized using the target registration error (TRE) after landmark-based registration under four deformation models (rigid, similarity, affine and thin-plate-spline [TPS]). The misalignment of histology sections from the front faces of tissue slices was quantified using manually identified landmarks. The impact of reconstruction models on the TRE after landmark-based reconstruction was measured under eight reconstruction models comprising one of four deformation models with and without constraining histology images to the tissue slice front faces. Results: Isotropic scaling improved the mean TRE by 0.8-1.0 mm (all results reported as 95% confidence intervals), while skew or TPS deformation improved the mean TRE by <0.1 mm. The mean misalignment was 1.1-1.9° (angle) and 0.9-1.3 mm (depth). Using isotropic scaling, the front face constraint raised the mean TRE by 0.6-0.8 mm. Conclusions: For sub-millimeter accuracy, 3D reconstruction models should not constrain histology images to the tissue slice front faces and should be flexible enough to model isotropic scaling.

Registration of in vivo prostate magnetic resonance images to digital histopathology images

Early and accurate diagnosis of prostate cancer enables minimally invasive therapies to cure the cancer with less morbidity. The purpose of this work is to non-rigidly register in vivo pre-prostatectomy prostate medical images to regionally-graded histopathology images from post-prostatectomy specimens, seeking a relationship between the multi parametric imaging and cancer distribution and aggressiveness. Our approach uses image-based registration in combination with a magnetically tracked probe to orient the physical slicing of the specimen to be parallel to the in vivo imaging planes, yielding a tractable 2D registration problem. We measured a target registration error of 0.85 mm, a mean slicing plane marking error of 0.7 mm, and a mean slicing error of 0.6 mm; these results compare favourably with our 2.2 mm diagnostic MR image thickness. Qualitative evaluation of in vivo imaging-histopathology fusion reveals excellent anatomic concordance between MR and digital histopathology.

3D prostate histology reconstruction: An evaluation of image-based and fiducial-based algorithms

PURPOSE Evaluation of in vivo prostate imaging modalities for determining the spatial distribution and aggressiveness of prostate cancer ideally requires accurate registration of images to an accepted reference standard, such as histopathological examination of radical prostatectomy specimens. Three-dimensional (3D) reconstruction of prostate histology facilitates these registration-based evaluations by reintroducing 3D spatial information lost during histology processing. Because the reconstruction accuracy may constrain the clinical questions that can be answered with these data, it is important to assess the tradeoffs between minimally disruptive methods based on intrinsic image information and potentially more robust methods based on extrinsic fiducial markers. METHODS Ex vivo magnetic resonance (MR) images and digitized whole-mount histology images from 12 radical prostatectomy specimens were used to evaluate four 3D histology reconstruction algorithms. 3D reconstructions were computed by registering each histology image to the corresponding ex vivo MR image using one of two similarity metrics (mutual information or fiducial registration error) and one of two search domains (affine transformations or a constrained subset thereof). The algorithms were evaluated for accuracy using the mean target registration error (TRE) computed from homologous intrinsic point landmarks (3-16 per histology section; 232 total) identified on histology and MR images, and for the sensitivity of TRE to rotational, translational, and scaling initialization errors. RESULTS The algorithms using fiducial registration error and mutual information had mean ± standard deviation TREs of 0.7 ± 0.4 and 1.2 ± 0.7 mm, respectively, and one algorithm using fiducial registration error and affine transforms had negligible sensitivities to initialization errors. The postoptimization values of the mutual information-based metric showed evidence of errors due to both the optimizer and the similarity metric, and variation of parameters of the mutual information-based metric did not improve its performance. CONCLUSIONS The extrinsic fiducial-based algorithm had lower mean TRE and lower sensitivity to initialization than the intrinsic intensity-based algorithm using mutual information. A model relating statistical power to registration error for certain imaging validation study designs estimated that a reconstruction algorithm with a mean TRE of 0.7 mm would require 27% fewer subjects than the method used to initialize the algorithms (mean TRE 1.3 ± 0.7 mm), suggesting the choice of reconstruction technique can have a substantial impact on the design of imaging validation studies, and on their overall cost.

3D reconstruction of prostate histology based on quantified tissue cutting and deformation parameters

Methods for 3D histology reconstruction from sparse 2D digital histology images depend on knowledge about the positions, orientations, and deformations of tissue slices due to the histology process. This work quantitatively evaluates typical assumptions about the position and orientation of whole-mount prostate histology sections within coarsely sliced tissue blocks and about the deformation of tissue during histological processing and sectioning. 3-5 midgland tissue blocks from each of 7 radical prostatectomy specimens were imaged using magnetic resonance imaging before histology processing. After standard whole-mount paraffin processing and sectioning, the resulting sections were digitised. Homologous anatomic landmarks were identified on 22 midgland histology and MR images. Orientations and depths of sections relative to the front faces of the tissue blocks were measured based on the best-fit plane through the landmarks on the MR images. The mean±std section orientation was 1.7±1.1° and the mean±std depth of the sections was 1.0±0.5 mm. Deformation was assessed by using four transformation models (rigid, rigid+scale, affine and thin-plate-spline (TPS)) to align landmarks from histology and MR images, and evaluating each by measuring the target registration error (TRE) using a leave-one-out cross-validation. The rigid transformation model had higher mean TRE (p<0.001) than the other models, and the rigid+scale and affine models had higher mean TRE than the TPS model (p<0.001 and p<0.01 respectively). These results informed the design and development of a method for 3D prostate histology reconstruction based on extrinsic strand-shaped fiducial markers which yielded a 0.7±0.4 mm mean±std TRE.

Tissue block MRI for slice orientation-independent registration of digital histology images to ex vivo MRI of the prostate

We present a method for the registration of whole-mount digital histology images to ex vivo MR images of the prostate that relaxes the requirement for control over specimen slicing orientation. The approach uses extrinsic fiducials visible on histology and MRI, as well as block MR images of tissue slices after coarse sectioning, to support a two-stage registration approach: (1) registration of digital histology images to block MR images, and (2) registration of block MR images to whole-specimen ex vivo MR images. This work presents a preliminary quantitative validation on 4 clinical prostate specimens, yielding target registration errors of 0.6 mm and 0.5 mm, respectively, for the registration stages, measured using intrinsic fiducials.

Validation of direct registration of whole-mount prostate digital histopathology to ex vivo MR images

Accurate determination of cancer stage and grade from in vivo prostate imaging could improve biopsy guidance, therapy selection and, possibly, focal therapy guidance. Validating prostate cancer imaging ideally requires accurate 3D registration of in vivo imaging to histopathology, which is facilitated by intermediate histology-ex vivo imaging registration. This work introduces and evaluates a direct registration with fiducialbased local refinement of digital prostate histopathology to ex vivo magnetic resonance (MR) images that obviates three elements typical of existing methods: (1) guidance of specimen slicing, (2) imaging/photography of sliced tissue blocks, and (3) registration guidance based on anatomical image features. The mean target registration error (TRE) of 98 intrinsic landmarks across 21 histology images was calculated for the proposed direct registration (0.7 mm) and compared to existing approaches: indirect using tissue block MR images (0.8 mm) and image-guided-slicing-based (1.0 mm). The local refinement was also shown to improve existing approaches to achieve a similar mean TRE (0.7 mm).

Multiparametric MR imaging of prostate cancer foci: assessing the detectability and localizability of Gleason 7 peripheral zone cancers based on image contrasts

Purpose: Multiparametric magnetic resonance imaging (MPMRI) supports detection and staging of prostate cancer, but the image characteristics needed for tumor boundary delineation to support focal therapy have not been widely investigated. We quantified the detectability (image contrast between tumor and non-cancerous contralateral tissue) and the localizability (image contrast between tumor and non-cancerous neighboring tissue) of Gleason score 7 (GS7) peripheral zone (PZ) tumors on MPMRI using tumor contours mapped from histology using accurate 2D–3D registration. Methods: MPMRI [comprising T2-weighted (T2W), dynamic-contrast-enhanced (DCE), apparent diffusion coefficient (ADC) and contrast transfer coefficient images] and post-prostatectomy digitized histology images were acquired for 6 subjects. Histology contouring and grading (approved by a genitourinary pathologist) identified 7 GS7 PZ tumors. Contours were mapped to MPMRI images using semi-automated registration algorithms (combined target registration error: 2 mm). For each focus, three measurements of mean ± standard deviation of image intensity were taken on each image: tumor tissue (mT±sT), non-cancerous PZ tissue < 5 mm from the tumor (mN±sN), and non-cancerous contralateral PZ tissue (mC±sC). Detectability [D, defined as mT-mC normalized by sT and sC added in quadrature] and localizability [L, defined as mT-mN normalized by sT and sN added in quadrature] were quantified for each focus on each image. Results: T2W images showed the strongest detectability, although detectability |D|≥1 was observed on either ADC or DCE images, or both, for all foci. Localizability on all modalities was variable; however, ADC images showed localizability |L|≥1 for 3 foci. Conclusions: Delineation of GS7 PZ tumors on individual MPMRI images faces challenges; however, images may contain complementary information, suggesting a role for fusion of information across MPMRI images for delineation.