A Survey of Methods for 3D Histology Reconstruction

Abstract

Histology permits the observation of otherwise invisible structures of the internal topography of a specimen. Although it enables the investigation of tissues at a cellular level, it is invasive and breaks topology due to cutting. Three-dimensional (3D) reconstruction was thus introduced to overcome the limitations of single-section studies in a dimensional scope. 3D reconstruction finds its roots in embryology, where it enabled the visualisation of spatial relationships of developing systems and organs, and extended to biomedicine, where the observation of individual, stained sections provided only partial understanding of normal and abnormal tissues. However, despite bringing visual awareness, recovering realistic reconstructions is elusive without prior knowledge about the tissue shape. 3D medical imaging made such structural ground truths available. In addition, combining non-invasive imaging with histology unveiled invaluable opportunities to relate macroscopic information to the underlying microscopic properties of tissues through the establishment of spatial correspondences; image registration is one technique that permits the automation of such a process and we describe reconstruction methods that rely on it. It is thereby possible to recover the original topology of histology and lost relationships, gain insight into what affects the signals used to construct medical images (and characterise them), or build high resolution anatomical atlases. This paper reviews almost three decades of methods for 3D histology reconstruction from serial sections, used in the study of many different types of tissue. We first summarise the process that produces digitised sections from a tissue specimen in order to understand the peculiarity of the data, the associated artefacts and some possible ways to minimise them. We then describe methods for 3D histology reconstruction with and without the help of 3D medical imaging, along with methods of validation and some applications. We finally attempt to identify the trends and challenges that the field is facing, many of which are derived from the cross-disciplinary nature of the problem as it involves the collaboration between physicists, histolopathologists, computer scientists and physicians.

Solving thewhereproblem and quantifying geometric variation in neuroanatomy using generative diffeomorphic mapping

“…Working with histology sections in particular leads to specific challenges as reviewed in 25 . Signal not present in reference images, such as tears or holes, are described as important, but are not addressed adequately by the surveyed methods.…”

Phosphotungstic acid‐enhanced microCT: Optimized protocols for embryonic and early postnatal mice

“…A series of 2D histological images may be rendered into an approximation of the original 3D structure only through a labor‐intensive process of registering and aligning each image. Even with recent advances in the automation of 3D registration and alignment of serial sections, the process still results in the destruction of the original specimen and can only minimize, but not eliminate, alignment errors and artificial deformations to biological structures . 3D structures and relationships can be visualized in specimens that are cleared and stained, but the clearing process renders the tissue friable, such that continued manipulation during observation can damage anatomical associations and structural relationships.…”

“…[1][2][3][4][5][6][7] Histology uses thin slices of embedded material that can be stained to highlight different cells or cellular components. A 3D block of biological material must necessarily be destroyed, by embedding the material in a harder substance that enables thin sections (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15) μm thick) to be produced from the block. The timeconsuming process of specimen preparation and sectioning introduces the potential for artifacts that can obscure or distort the tissues and tissue relationships, including tearing, fracturing, or folding of the tissue.…”

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“…There are a few studies that suggest the importance and relevance of studying the 3D architecture of the tissue for enhancing our understanding of disease processes. As more of these "voxel" based deep learning algorithms are published, 3D reconstructions from sequential 5 μm tissue sections are increasingly becoming relevant in investigations of disease biology, prognosis, and potential for cancer metastasis 11,12 . Finally, having aligned, differentially stained slides for the pathologist to rapidly switch back and forth between (a very common task that pathologists do manually with glass slides) could improve productivity and clinical workflow for pathologists.…”