Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Shojaii, Rushin; Bacopulos, Stephanie; Yang, Wenyi; Karavardanyan, Tigran; Spyropoulos, Demetri D.; Raouf, Afshin; Martel, Anne L.; Seth, Arun

doi:10.3791/51325

Cited by 8 publications

(10 citation statements)

References 28 publications

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“…However, their detection relies on accurate segmentations, which in turn may be affected by intensity inhomogeneities if, for example, a simple global threshold is to be used for all the slices. Contours of tissues were obtained automatically by thresholding in Shojaii et al (2014); Shojaii and Martel (2009); Cohen et al (1998). They were manually edited (if necesssary) in Cohen et al (1998) and modeled using B-splines; the inverse chord length method (Cohen Fernand et al, 1995), which regulates the number of knot points based on the amount of shape variation a region is subjected to, was used to select the set that best described a given curve (fewer knots when the variation is small).…”

Section: Detecting Points Of Interestmentioning

confidence: 99%

A Survey of Methods for 3D Histology Reconstruction

Pichat

Iglesias

Yousry³

et al. 2018

Medical Image Analysis

165

179

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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.

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Section: Detecting Points Of Interestmentioning

confidence: 99%

A Survey of Methods for 3D Histology Reconstruction

Pichat

Iglesias

Yousry³

et al. 2018

Medical Image Analysis

165

179

View full text Add to dashboard Cite

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“…In our previous work, 3,26 we developed a pipeline for histology volume reconstruction, which used optical blockface images as reference images. Blockface images were obtained by photographing the surface of the tissue block before each section was cut, and the image of each histology section was then registered to its correspondent blockface image using 2-D rigid and deformable registration techniques.…”

Section: Related Workmentioning

confidence: 99%

“…In previous work, we used an edge-based registration method 3,26 to align histology and blockface images from smaller murine tissue blocks. Here, we apply the same method to the wholemount lumpectomy images to make a comparison with the SIFTFlow method.…”

Section: Registration Using Edge Pointsmentioning

confidence: 99%

“…First, they can provide structural information at the microscopic resolution. For example, biologists have used this approach to study the morphology of embryonic development, 1 the coronary system, 2 and mammary glands 3 in murine models, and 3-D histology reconstruction has also been applied to human breast cancer in the study of ductal carcinoma in situ (DCIS). 4 A 3-D reconstruction also has the potential to improve our understanding of heterogeneity in tumors by allowing us to visualize the spatial distribution of different tumor markers.…”

Section: Introductionmentioning

confidence: 99%

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Optimized SIFTFlow for registration of whole-mount histology to reference optical images

Shojaii

Martel

2016

J. Med. Imag

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Abstract. The registration of two-dimensional histology images to reference images from other modalities is an important preprocessing step in the reconstruction of three-dimensional histology volumes. This is a challenging problem because of the differences in the appearances of histology images and other modalities, and the presence of large nonrigid deformations which occur during slide preparation. This paper shows the feasibility of using densely sampled scale-invariant feature transform (SIFT) features and a SIFTFlow deformable registration algorithm for coregistering whole-mount histology images with blockface optical images. We present a method for jointly optimizing the regularization parameters used by the SIFTFlow objective function and use it to determine the most appropriate values for the registration of breast lumpectomy specimens. We demonstrate that tuning the regularization parameters results in significant improvements in accuracy and we also show that SIFTFlow outperforms a previously described edge-based registration method. The accuracy of the histology images to blockface images registration using the optimized SIFTFlow method was assessed using an independent test set of images from five different lumpectomy specimens and the mean registration error was 0.32 AE 0.22 mm. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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“…85 Related approaches in terms of target size include methods focused on small organs. Several 86 studies have proposed combined MRI-histology approaches for the prostate [36,37], lymphoid 87 structures [38], mammary glands [39], and kidneys [40]. Another comparable application is the 88 study of small animal brains, in particular rodents [41][42][43] and small monkeys [44,45].…”

mentioning

confidence: 99%

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

Mancini

Casamitjana

Peter

et al. 2020

Preprint

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Ex vivo imaging enables analysis of the human brain at a level of detail that is not possible in vivo with MRI. In particular, histology can be used to study brain tissue at the microscopic level, using a wide array of different stains that highlight different microanatomical features. Complementing MRI with histology has important applications in ex vivo atlas building and in modeling the link between microstructure and macroscopic MR signal. However, histology requires sectioning tissue, hence distorting its 3D structure, particularly in larger human samples. Here, we present an open-source computational pipeline to produce 3D consistent histology reconstructions of the human brain. The pipeline relies on a volumetric MRI scan that serves as undistorted reference, and on an intermediate imaging modality (blockface photography) that bridges the gap between MRI and histology. We present results on 3D histology reconstruction of a whole human hemisphere.1/26 33 1. Cutting and sectioning of tissue for histology introduces distortions (stretching, tearing, 34 folding, cracking, see [6]) that are specific to each section. Therefore, an external volumetric 35 reference (typically an MRI scan) is required to produce an unbiased registration. Without 36 such a reference, naïve pairwise registration leads to accumulation of errors ("z-shift") and 37 spurious straightening of curved structures (the so-called "banana effect", [7]). 382. Large differences in contrast and resolution between MRI and histology, combined with 39 potential inhomogeneous staining and the aforementioned sectioning artifacts, make the 40 alignment of these two modalities a difficult inter-modality registration problem. 413. The large size of the human brain, compared with most animal models, requires cutting 42 the tissue in blocks for whole-brain analyses, with the resulting need to reassemble the 43 blocks [6], which is a part-to-whole registration problem [8,9]. This problem can be solved 44 with whole brain microtomes, although such microtomes are only available in few selected 45 sites around the world. Moreover, they exacerbate the sectioning artifacts, due to the much 46 larger surface area of the sections. 47Even when a reference MRI is available, 3D histology reconstruction is an ill-posed problem, 48 as errors in the -typically linear -registration between the MRI and the histological stack 49 2/26

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Reconstruction of 3-Dimensional Histology Volume and its Application to Study Mouse Mammary Glands

Cited by 8 publications

References 28 publications

A Survey of Methods for 3D Histology Reconstruction

A Survey of Methods for 3D Histology Reconstruction

Optimized SIFTFlow for registration of whole-mount histology to reference optical images

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

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