3D volume reconstruction from serial breast specimen radiographs for mapping between histology and 3D whole specimen imaging

Abstract

Purpose: In breast imaging, radiological in vivo images, such as x-ray mammography and magnetic resonance imaging (MRI), are used for tumor detection, diagnosis, and size determination. After excision, the specimen is typically sliced into slabs and a small subset is sampled. Histopathological imaging of the stained samples is used as the gold standard for characterization of the tumor microenvironment. A 3D volume reconstruction of the whole specimen from the 2D slabs could facilitate bridging the gap between histology and in vivo radiological imaging. This task is challenging, however, due to the large deformation that the breast tissue undergoes after surgery and the significant undersampling of the specimen obtained in histology. In this work, we present a method to reconstruct a coherent 3D volume from 2D digital radiographs of the specimen slabs. Methods: To reconstruct a 3D breast specimen volume, we propose the use of multiple target neighboring slices, when deforming each 2D slab radiograph in the volume, rather than performing pairwise registrations. The algorithm combines neighborhood slice information with free-form deformations, which enables a flexible, nonlinear deformation to be computed subject to the constraint that a coherent 3D volume is obtained. The neighborhood information provides adequate constraints, without the need for any additional regularization terms. Results: The volume reconstruction algorithm is validated on clinical mastectomy samples using a quantitative assessment of the volume reconstruction smoothness and a comparison with a whole specimen 3D image acquired for validation before slicing. Additionally, a target registration error of 5 mm (comparable to the specimen slab thickness of 4 mm) was obtained for five cases. The error was computed using manual annotations from four observers as gold standard, with interobserver variability of 3.4 mm. Finally, we illustrate how the reconstructed volumes can be used to map histology images to a 3D specimen image of the whole sample (either MRI or CT). Conclusions: Qualitative and quantitative assessment has illustrated the benefit of using our proposed methodology to reconstruct a coherent specimen volume from serial slab radiographs. To our knowledge, this is the first method that has been applied to clinical breast cases, with the goal of reconstructing a whole specimen sample. The algorithm can be used as part of the pipeline of mapping histology images to ex vivo and ultimately in vivo radiological images of the breast.

VERDICT MRI validation in fresh and fixed prostate specimens using patient‐specific moulds for histological and MR alignment

“…The T 2 ‐weighted image from the reference slice of the fresh specimen was registered to the T 2 ‐weighted image of the fixed specimen by a 2D rigid registration using a block‐matching strategy and correlation coefficient as similarity measure …”

Mapping Mouse Brain Slice Sequence to a Reference Brain Without 3D Reconstruction

“…This prior work first reconstructs an initial volume estimate from the slices and then registers this reconstructed volume to the reference. Some works focus on the reconstruction problem because registration between a reconstructed volume and a reference is relatively standard (Stille et al, 2013;Dauguet et al, 2007;Mertzanidou et al, 2017); other works discuss on the reconstruction problem in an absence of reference volumes (Ourselin et al, 2001;Cifor et al, 2011;Ju et al, 2006;Bagci and Bai, 2010). Initial work reconstructed the experimental volume by pairwise registration of adjacent slices (Stille et al, 2013;Ourselin et al, 2001;Cifor et al, 2011).…”

“…For example, Ju et al (Ju et al, 2006) reduced error propagation by warping each slice with a weighted linear and nonlinear combination of warp fields to multiple adjacent slices. Others use blockface images (Dauguet et al, 2007) or select internal reference slices to reconstruct small chunks and then put together the entire volume (Bagci and Bai, 2010;Mertzanidou et al, 2017). However, with almost every slice at least slightly distorted, internal nonrigid registration will likely change the original shape of biological structures.…”

A multimodal computational pipeline for 3D histology of the human brain

“…MRI does not yield microscopic resolution, but produces undistorted 3D volumes. 23 Therefore, the combination of these two modalities offers a solution to the problem of imaging 24 the human brain at high resolution in 3D. In fact, successful large-scale projects like BigBrain [4] 25 or the Allen Atlas [5] have shown important advancements in terms of creating new whole-brain 26 atlases with cellular-level resolution.…”

“…71 Despite not being spatially comprehensive, approaches based on selected regions of interest 72 (ROIs) have a high clinical relevance, mainly because of their potential applications in oncology. 73 It is no surprise, then, that several studies have combined MRI with histopathology for cancer 74 applications: examples include breast cancer [23,24], pancreatic tumors [25] and gliomas [26]. 75 These examples lay the foundation for future 3D histopathology, especially given the parallel 76 effort in tridimensional reconstruction for confocal microscopy [27].…”