Validation of histology image registration

Shojaii, Rushin; Karavardanyan, Tigran; Yaffe, Martin J.; Martel, Anne L.

doi:10.1117/12.878762

Cited by 6 publications

(10 citation statements)

References 15 publications

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“…In this pipeline we use blockface images as a set of reference images for histology volume reconstruction 19 . Blockface images are taken of the paraffin tissue blocks after mounting on the microtome and before each section is cut.…”

Section: Introductionmentioning

confidence: 99%

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

Shojaii

Bacopulos

Yang

et al. 2014

JoVE

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Histology volume reconstruction facilitates the study of 3D shape and volume change of an organ at the level of macrostructures made up of cells. It can also be used to investigate and validate novel techniques and algorithms in volumetric medical imaging and therapies. Creating 3D high-resolution atlases of different organs(1,2,3) is another application of histology volume reconstruction. This provides a resource for investigating tissue structures and the spatial relationship between various cellular features. We present an image registration approach for histology volume reconstruction, which uses a set of optical blockface images. The reconstructed histology volume represents a reliable shape of the processed specimen with no propagated post-processing registration error. The Hematoxylin and Eosin (H&E) stained sections of two mouse mammary glands were registered to their corresponding blockface images using boundary points extracted from the edges of the specimen in histology and blockface images. The accuracy of the registration was visually evaluated. The alignment of the macrostructures of the mammary glands was also visually assessed at high resolution. This study delineates the different steps of this image registration pipeline, ranging from excision of the mammary gland through to 3D histology volume reconstruction. While 2D histology images reveal the structural differences between pairs of sections, 3D histology volume provides the ability to visualize the differences in shape and volume of the mammary glands.

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Section: Introductionmentioning

confidence: 99%

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

Shojaii

Bacopulos

Yang

et al. 2014

JoVE

Self Cite

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“…We used implanted fiducial markers 27 to measure the registration accuracy in mouse xenograft specimens and found that the deformable registration generated using the boundary points actually degraded the accuracy of the global rigid registration. 26 This highlights the need to improve the deformable registration step in the pipeline.…”

Section: Related Workmentioning

confidence: 99%

“…This paper focuses on the first step in a 3-D volume reconstruction pipeline, which is the correction of deformations in the whole-mount images using a reference blockface image. In our previous work, we automatically identified points at the boundary between the tissue specimen and the surrounding paraffin block in both histology and blockface images and used these to generate a deformation field, 26 but the results were disappointing. Here, we propose the use of an automatic featurebased image registration method, SIFTFlow, developed by Liu et al, 30,31 to align the whole-mount and blockface images.…”

Section: Our Approachmentioning

confidence: 99%

“…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%

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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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Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains

Wodziński

Skalski

2020

Phys. Med. Biol.

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The use of multiple dyes during histological sample preparation can reveal distinct tissue properties. However, since the slide preparation differs for each dye, the tissue slides are being deformed and a nonrigid registration is required before further processing. The registration of histology images is complicated because of: (i) a high resolution of histology images, (ii) complex, large, nonrigid deformations, (iii) difference in the appearance and partially missing data due to the use of multiple dyes. In this work, we propose a multistep, automatic, nonrigid image registration method dedicated to histology samples acquired with multiple stains. The proposed method consists of a feature-based affine registration, an exhaustive rotation alignment, an iterative, intensity-based affine registration, and a nonrigid alignment based on modality independent neighbourhood descriptor coupled with the Demons algorithm. A dedicated failure detection mechanism is proposed to make the method fully automatic, without the necessity of any manual interaction. The described method was proposed by the AGH team during the Automatic Non-rigid Histological Image Registration (ANHIR) challenge. The ANHIR dataset consists of 481 image pairs annotated by histology experts. Moreover, the ANHIR challenge submissions were evaluated using an independent, server-side evaluation tool. The main evaluation criteria was the target registration error normalized by the image diagonal. The median of median target registration error is below 0.19%. The proposed method is currently the second-best in terms of the average ranking of median target registration error, without statistically significant differences compared to the top-ranked method. We provide an open access to the method software and used parameters, making the results fully reproducible.

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Validation of histology image registration

Cited by 6 publications

References 15 publications

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

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

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

Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains

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