Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition

Peng, Tingying; Wang, Lichao; Bayer, Christine; Conjeti, Sailesh; Baust, Maximilian; Navab, Nassir

doi:10.1007/978-3-319-10404-1_5

Cited by 11 publications

(6 citation statements)

References 12 publications

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“…Correction of uneven illumination in histological slices has borrowed ideas from intensity inhomogeneity correction in MRI (Sled et al, 1998;Vovk et al, 2007). The correction of vignetting was addressed by Peng et al (2014) and Piccinini et al (2013b), and the interested reader may also refer to Reyes-Aldasoro (2009); Yu (2004). In the histology reconstruction literature, shading correction was performed as preprocessing in Bürgel et al (1999) using methods from Gonzalez (1987).…”

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

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“…The synthetic images are generated using a previously published simulation algorithm with some modifications (Lehmussola et al ., ). This image simulation algorithm is also used in (Peng et al., ). Gaussian noise δ is added into the simulated images consisting of two classes: background and cells.…”

Section: Resultsmentioning

confidence: 99%

Comparative evaluation of performance measures for shading correction in time‐lapse fluorescence microscopy

Liu

Kan

Hodgkin

2016

Journal of Microscopy

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Time-lapse fluorescence microscopy is a valuable technology in cell biology, but it suffers from the inherent problem of intensity inhomogeneity due to uneven illumination or camera nonlinearity, known as shading artefacts. This will lead to inaccurate estimates of single-cell features such as average and total intensity. Numerous shading correction methods have been proposed to remove this effect. In order to compare the performance of different methods, many quantitative performance measures have been developed. However, there is little discussion about which performance measure should be generally applied for evaluation on real data, where the ground truth is absent. In this paper, the state-of-the-art shading correction methods and performance evaluation methods are reviewed. We implement 10 popular shading correction methods on two artificial datasets and four real ones. In order to make an objective comparison between those methods, we employ a number of quantitative performance measures. Extensive validation demonstrates that the coefficient of joint variation (CJV) is the most applicable measure in time-lapse fluorescence images. Based on this measure, we have proposed a novel shading correction method that performs better compared to well-established methods for a range of real data tested.

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“…In addition, postprocessing techniques for reducing stripe, grid, or tiling artifacts have been a wide area of research in the field of aerial imaging and microscopy. These methods include advanced image registration and stitching methods, [8][9][10] flat-field (FF)-based processing, 11 fusion-based techniques, 12 Fourier-based filtering, 13 shading correction, 14 and deep learning methods. 15 While a few tile artifact correction methods for this type of imaging are available, one major challenge is determining which, if any, of these correction methods will provide adequate correction while preserving the surrounding image data.…”

Section: (C)]mentioning

confidence: 99%

Combined flat-field and frequency filter approach to correcting artifacts of multichannel two-photon microscopy

Knapp,

Lima,

Daigle

et al. 2024

J. Biomed. Opt.

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Significance: Multiphoton microscopy (MPM) is a useful biomedical imaging tool for its ability to probe labeled and unlabeled depth-resolved tissue biomarkers at high resolution. Automated MPM tile scanning allows for whole-slide image acquisition but can suffer from tile-stitching artifacts that prevent accurate quantitative data analysis.Aim: We have investigated postprocessing artifact correction methods using ImageJ macros and custom Python code. Quantitative and qualitative comparisons of these methods were made using whole-slide MPM autofluorescence and secondharmonic generation images of human duodenal tissue.Approach: Image quality after artifact removal is assessed by evaluating the processed image and its unprocessed counterpart using the root mean square error, structural similarity index, and image histogram measurements.Results: Consideration of both quantitative and qualitative results suggest that a combination of a custom flat-field-based correction and frequency filtering processing step provide improved artifact correction when compared with each method used independently to correct for tiling artifacts of tile-scan MPM images.Conclusions: While some image artifacts remain with these methods, further optimization of these processing steps may result in computational-efficient methods for removing these artifacts that are ubiquitous in large-scale MPM imaging. Removal of these artifacts with retention of the original image information would facilitate the use of this imaging modality in both research and clinical settings, where it is highly useful in collecting detailed morphologic and optical properties of tissue.

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Shading Correction for Whole Slide Image Using Low Rank and Sparse Decomposition

Cited by 11 publications

References 12 publications

A Survey of Methods for 3D Histology Reconstruction

A Survey of Methods for 3D Histology Reconstruction

Comparative evaluation of performance measures for shading correction in time‐lapse fluorescence microscopy

Combined flat-field and frequency filter approach to correcting artifacts of multichannel two-photon microscopy

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