Interpolation of Histological Slices by Means of Non-rigid Registration

Gaffling, Simone; Jager, F.C.A. den Hartog; Daum, Volker; Tauchi, Miyuki; Lütjen–Drecoll, Elke

doi:10.1007/978-3-540-93860-6_54

Cited by 8 publications

(13 citation statements)

References 3 publications

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“…Then, Malandain et al estimated the optimal affine intensity transformation between them (though higher order polynomial fits may be used). This type of method can be applied in different ways and the reference slice is usually picked for its relative smooth intensity variation of staining and high-contrasted structures (Gaffling et al, 2009;Yang et al, 2013). Adler et al (2014) optimised the parameters of a global affine intensity transformation using white and grey matter masked images jointly.…”

Section: Preprocessing Of Digital Pathologymentioning

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: Preprocessing Of Digital Pathologymentioning

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

“…Strategies for this include eliminating the slice if the loss of anatomical information is tolerable, repairing by complete or partial interpolation of the defect slice, cmp. [45],[36],[27], or by using artifact-tolerant non-rigid registration methods [46], [47]. For medium-sized defects, masking the defect image parts in the registration might also be sufficient to achieve a satisfactory result.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…After 6 iterations, the effect was visible from slices 21 to 39. When replacing the defective slice with an interpolated one [36] before applying our method, the morphology in the reconstructed image stack again seems to be intact, with only small differences to the original reconstruction.…”

Section: Evaluation and Resultsmentioning

confidence: 99%

“…The t -th approximation of image I z is not given by merely deforming the previous image, as indicated in Eq. (10), but instead this approximation is used as interpolated intermediate image Î z ( t ) , as described in [36]. This intermediate image is then used as reference, to which the original histology image is non-rigidly registered.…”

Section: Methodsmentioning

confidence: 99%

“…Just removing such an image from the stack is not an option, since this would compress the anatomy at this position. Instead, we use interpolation of slices as outlined in [36] to fill the gap. This restores the uniformity of the subintervals, and therefore the convergence requirements outlined above.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction

Gaffling¹,

Daum

Steidl

et al. 2015

IEEE Trans. Med. Imaging

Self Cite

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Three-dimensional (3-D) reconstruction of histological slice sequences offers great benefits in the investigation of different morphologies. It features very high-resolution which is still unmatched by in-vivo 3-D imaging modalities, and tissue staining further enhances visibility and contrast. One important step during reconstruction is the reversal of slice deformations introduced during histological slice preparation, a process also called image unwarping. Most methods use an external reference, or rely on conservative stopping criteria during the unwarping optimization to prevent straightening of naturally curved morphology. Our approach shows that the problem of unwarping is based on the superposition of low-frequency anatomy and high-frequency errors. We present an iterative scheme that transfers the ideas of the Gauss-Seidel method to image stacks to separate the anatomy from the deformation. In particular, the scheme is universally applicable without restriction to a specific unwarping method, and uses no external reference. The deformation artifacts are effectively reduced in the resulting histology volumes, while the natural curvature of the anatomy is preserved. The validity of our method is shown on synthetic data, simulated histology data using a CT data set and real histology data. In the case of the simulated histology where the ground truth was known, the mean Target Registration Error (TRE) between the unwarped and original volume could be reduced to less than 1 pixel on average after 6 iterations of our proposed method.

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Three‐dimensional histology reveals dissociable human hippocampal long‐axis gradients of Alzheimer's pathology

Ortega‐Cruz,

Bress,

Gazula

et al. 2024

Alzheimer's & Dementia

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INTRODUCTIONThree‐dimensional (3D) histology analyses are essential to overcome sampling variability and understand pathological differences beyond the dissection axis. We present Path2MR, the first pipeline allowing 3D reconstruction of sparse human histology without a magnetic resonance imaging (MRI) reference. We implemented Path2MR with post‐mortem hippocampal sections to explore pathology gradients in Alzheimer's disease.METHODSBlockface photographs of brain hemisphere slices are used for 3D reconstruction, from which an MRI‐like image is generated using machine learning. Histology sections are aligned to the reconstructed hemisphere and subsequently to an atlas in standard space.RESULTSPath2MR successfully registered histological sections to their anatomic position along the hippocampal longitudinal axis. Combined with histopathology quantification, we found an expected peak of tau pathology at the anterior end of the hippocampus, whereas amyloid‐beta (Aβ) displayed a quadratic anterior‐posterior distribution.CONCLUSIONPath2MR, which enables 3D histology using any brain bank data set, revealed significant differences along the hippocampus between tau and Aβ.Highlights Path2MR enables three‐dimensional (3D) brain reconstruction from blockface dissection photographs. This pipeline does not require dense specimen sampling or a subject‐specific magnetic resonance (MR) image. Anatomically consistent mapping of hippocampal sections was obtained with Path2MR. Our analyses revealed an anterior‐posterior gradient of hippocampal tau pathology. In contrast, the peak of amyloid‐beta (Aβ) deposition was closer to the hippocampal body.

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Interpolation of Histological Slices by Means of Non-rigid Registration

Cited by 8 publications

References 3 publications

A Survey of Methods for 3D Histology Reconstruction

A Survey of Methods for 3D Histology Reconstruction

A Gauss-Seidel Iteration Scheme for Reference-Free 3-D Histological Image Reconstruction

Three‐dimensional histology reveals dissociable human hippocampal long‐axis gradients of Alzheimer's pathology

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