Registration of histological whole slide images guided by vessel structures

Schwier, Michael; Böhler, Tobias; Hahn, Horst K.; Dahmen, Uta; Dirsch, Olaf

doi:10.4103/2153-3539.109868

Cited by 33 publications

(28 citation statements)

References 13 publications

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“…The error made after reconstruction is then computed (Cifor et al, 2011;Nikou et al, 2003;Ju et al, 2006;Braumann et al, 2005;Majka and Wójcik, 2015;Bagci and Bai, 2010). Synthetic datasets (e.g., phantom models) were used in Schwier et al (2013); Ou and Davatzikos (2009); Arganda-Carreras et al (2010). Comparison against manually realigned stack (by an expert physician researcher) can also be performed (Krinidis et al, 2003a;Groen et al, 2010).…”

Section: Validation Methodsmentioning

confidence: 99%

“…Using high-level features (i) The Euclidean distance in combination with other criteria, like the size of structures, is used to assess the similarity between pairs of such features. Schwier et al (2013) tried all possible combinations of pairs in adjacent sections. The transformation that gave the best similarity was kept.…”

Section: Correspondences and Spatial Transformationsmentioning

confidence: 99%

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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: Validation Methodsmentioning

confidence: 99%

Section: Correspondences and Spatial Transformationsmentioning

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

show abstract

“…In the work by Schwier at al. [16], for example, vessels are extracted from each image and nonrigid registration is performed using the vessel masks. The work by Liang et al [17] combines all the three approaches and proposes a multi-scale registration algorithm which rigidly aligns the patches, fuses the rigid transformations by a cubic b-spline deformation and performs vessel segmentation and association later on to reduce the registration error.…”

Section: Figurementioning

confidence: 99%

Regional Registration of Whole Slide Image Stacks Containing Highly Deformed Artefacts

Paknezhad

Loh²,

Choudhury³

et al. 2020

Preprint

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Motivation High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions that each individual tissue slice experiences while cutting and mounting the tissue on the glass slide. Performing registration for the whole tissue slices may be adversely affected by the deformed tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose an accurate and robust regional registration algorithm for whole slide images which incrementally focuses registration on the area around the region of interest. Results Using mean similarity index as the metric, the proposed algorithm (mean ± std: 0.84 ± 0.11) followed by a fine registration algorithm (0.86 ± 0.08) outperformed the state-of-the-art linear whole tissue registration algorithm (0.74±0.19) and the regional version of this algorithm (0.81±0.15). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: 0.82 ± 0.12, regional: 0.77 ± 0.22) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: 0.79 ± 0.16 , patch size 512: 0.77 ± 0.16) for medical images. Availability The C++ implementation code is available online at the github repository: https://github.com/MahsaPaknezhad/WSIRegistration

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“…However, hepatocytes laden with PAS-positive glycogen granules partially lose contrast between the cytoplasm and cell membrane. For some applications, complex staining protocols enable the interpretation of the morphometry of hepatocytes within the context of the liver tissue (Hoehme et al 2010;Schwier et al 2013;Hammad et al 2014). Moreover, whole-slide imaging (Hoehme et al 2010;Schwier et al 2013;Hammad et al 2014) is a good choice to handle the variability in the level of more histological sections.…”

Section: Study Implicationsmentioning

confidence: 99%

Stereological analysis of size and density of hepatocytes in the porcine liver

et al. 2016

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The porcine liver is frequently used as a large animal model for verification of surgical techniques, as well as experimental therapies. Often, a histological evaluation is required that include measurements of the size, nuclearity or density of hepatocytes. Our aims were to assess the mean number-weighted volume of hepatocytes, the numerical density of hepatocytes, and the fraction of binuclear hepatocytes (BnHEP) in the porcine liver, and compare the distribution of these parameters among hepatic lobes and macroscopic regions of interest (ROIs) with different positions related to the liver vasculature. Using disector and nucleator as design-based stereological methods, the morphometry of hepatocytes was quantified in seven healthy piglets. The samples were obtained from all six hepatic lobes and three ROIs (peripheral, paracaval and paraportal) within each lobe. Histological sections (thickness 16 μm) of formalin-fixed paraffin-embedded material were stained with the periodic acid-Schiff reaction to indicate the cell outlines and were assessed in a series of 3-μm-thick optical sections. The mean number-weighted volume of mononuclear hepatocytes (MnHEP) in all samples was 3670 ± 805 μm (mean ± SD). The mean number-weighted volume of BnHEP was 7050 ± 2550 μm . The fraction of BnHEP was 4 ± 2%. The numerical density of all hepatocytes was 146 997 ± 15 738 cells mm of liver parenchyma. The porcine hepatic lobes contained hepatocytes of a comparable size, nuclearity and density. No significant differences were identified between the lobes. The peripheral ROIs of the hepatic lobes contained the largest MnHEP with the smallest numerical density. The distribution of a larger MnHEP was correlated with a larger volume of BnHEP and a smaller numerical density of all hepatocytes. Practical recommendations for designing studies that involve stereological evaluations of the size, nuclearity and density of hepatocytes in porcine liver are provided.

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Registration of histological whole slide images guided by vessel structures

Cited by 33 publications

References 13 publications

A Survey of Methods for 3D Histology Reconstruction

A Survey of Methods for 3D Histology Reconstruction

Regional Registration of Whole Slide Image Stacks Containing Highly Deformed Artefacts

Stereological analysis of size and density of hepatocytes in the porcine liver

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