Stain deconvolution using statistical analysis of multi-resolution stain colour representation

Stain colour estimation is a prominent factor of the analysis pipeline in most of histology image processing algorithms. Providing a reliable and efficient stain colour deconvolution approach is fundamental for robust algorithm. In this paper, we propose a novel method for stain colour deconvolution of histology images. This approach statistically analyses the multi-resolutional representation of the image to separate the independent observations out of the correlated ones. We then estimate the stain mixing matrix using filtered uncorrelated data. We conducted an extensive set of experiments to compare the proposed method to the recent state of the art methods and demonstrate the robustness of this approach using three different datasets of scanned slides, prepared in different labs using different scanners

Simultaneous automatic scoring and co-registration of hormone receptors in tumour areas in whole slide images of breast cancer tissue slides

Aims: Automation of downstream analysis may offer many potential benefits to routine histopathology. One area of interest for automation is in the scoring of multiple immunohistochemical markers in order to predict the patient's response to targeted therapies. Automated serial slide analysis of this kind requires robust registration to identify common tissue regions across sections. We present an automated method for co-localised scoring of Estrogen Receptor and Progesterone Receptor (ER/PR) in breast cancer core biopsies using whole slide images. Methods and Results: Regions of tumour in a series of fifty consecutive breast core biopsies were identified by annotation on H&E whole slide images. Sequentially cut immunohistochemical stained sections were scored manually, before being digitally scanned and then exported into JPEG 2000 format. A two-stage registration process was performed to identify the annotated regions of interest in the immunohistochemistry sections, which were then scored using the Allred system. Overall correlation between manual and automated scoring for ER and PR was 0.944 and 0.883 respectively, with 90% of ER and 80% of PR scores within in one point or less of agreement. Conclusions: This proof of principle study indicates slide registration can be used as a basis for automation of the downstream analysis for clinically relevant biomarkers in the majority of cases. The approach is likely to be improved by implantation of safeguarding analysis steps post registration

Regional Histopathology and Prostate MRI Positivity: A Secondary Analysis of the PROMIS Trial

Background: The effects of regional histopathologic changes on prostate MRI scans have not been accurately quantified in men with an elevated prostate-specific antigen (PSA) level and no previous biopsy. / Purpose: To assess how Gleason grade, maximum cancer core length (MCCL), inflammation, prostatic intraepithelial neoplasia (PIN), or atypical small acinar proliferation within a Barzell zone affects the odds of MRI visibility. / Materials and Methods: In this secondary analysis of the Prostate MRI Imaging Study (PROMIS; May 2012 to November 2015), consecutive participants who underwent multiparametric MRI followed by a combined biopsy, including 5-mm transperineal mapping (TPM), were evaluated. TPM pathologic findings were reported at the whole-prostate level and for each of 20 Barzell zones per prostate. An expert panel blinded to the pathologic findings reviewed MRI scans and declared which Barzell areas spanned Likert score 3–5 lesions. The relationship of Gleason grade and MCCL to zonal MRI outcome (visible vs nonvisible) was assessed using generalized linear mixed-effects models with random intercepts for individual participants. Inflammation, PIN, and atypical small acinar proliferation were similarly assessed in men who had negative TPM results. / Results: Overall, 161 men (median age, 62 years [IQR, 11 years]) were evaluated and 3179 Barzell zones were assigned MRI status. Compared with benign areas, the odds of MRI visibility were higher when a zone contained cancer with a Gleason score of 3+4 (odds ratio [OR], 3.1; 95% CI: 1.9, 4.9; P < .001) or Gleason score greater than or equal to 4+3 (OR, 8.7; 95% CI: 4.5, 17.0; P < .001). MCCL also determined visibility (OR, 1.24 per millimeter increase; 95% CI: 1.15, 1.33; P < .001), but odds were lower with each prostate volume doubling (OR, 0.7; 95% CI: 0.5, 0.9). In men who were TPM-negative, the presence of PIN increased the odds of zonal visibility (OR, 3.7; 95% CI: 1.5, 9.1; P = .004). / Conclusion: An incremental relationship between cancer burden and prostate MRI visibility was observed. Prostatic intraepithelial neoplasia contributed to false-positive MRI findings

Registration and multi-immunohistochemical analysis of whole slide images of serial tissue sections.

The identification and classification of tissue abnormalities for the purpose of disease diagnosis have been greatly served by the discipline of histopathology, and Immunohistochemistry (IHC) in particular. The advent of digital slide scanners and computerised slide viewing software have opened the door for introducing automated algorithms into what has traditionally been a predominantly manual discipline. Multi-IHC analysis is one potential area of interest for automation, which will be discussed in detail in this work. Analysis occurs on serial sections of tissue, which must be realigned before their IHC marker expressions can be compared directly. This requires a robust method of serial section registration. Two methods of automated serial section registration are present, which are each designed to align a particular tissue type: breast core biopsy sections or resected colorectal cancer sections. Automated multi-IHC analysis is presented from the perspective of two case studies: Scoring of Oestrogen Receptor and Progesterone Receptor (ER/PR) on breast core biopsies and IHC scoring and colocalisation of resected colorectal cancer (CRC) sections. For each case study the background of the problem is introduced, followed by a discussion of how each type of analysis is performed in clinical practice, and it is then explained how this is implemented as an automated algorithm. For the scoring of ER/PR, it is shown that the algorithm can achieve good agreement with a pathologist on a sample of 50 cases, which suggests that automated ER/PR scoring is suitable for clinical practice. For the analysis of CRC, the results of scoring and colocalisation are shown in the form of localised maps with a discussion into how they may be used for further analysis. As part of this framework a number of additional steps must be carried out before the goal of multi-IHC analysis can be realised. Two pre-processing steps, both of which are key to ensuring that the end results are of the highest quality, are presented: Tissue Segmentation and Out of Focus Area detection. A complete Out of Focus Area detection system is presented, which has led to the development of a Windows software that is currently being used in a local hospital. In addition, we present an automated method of Stain Separation, based around Independent Component Analysis, which allows us to extract and process the IHC marker expressions directly. This method includes a novel correction process to improve any faults in the primary analysis

Hyper-stain inspector : a framework for robust registration and localised co-expression analysis of multiple whole-slide images of serial histology sections

In this paper, we present a fast method for registration of multiple large, digitised whole-slide images (WSIs) of serial histology sections. Through cross-slide WSI registration, it becomes possible to select and analyse a common visual field across images of several serial section stained with different protein markers. It is, therefore, a critical first step for any downstream co-localised cross-slide analysis. The proposed registration method uses a two-stage approach, first estimating a fast initial alignment using the tissue sections’ external boundaries, followed by an efficient refinement process guided by key biological structures within the visual field. We show that this method is able to produce a high quality alignment in a variety of circumstances, and demonstrate that the refinement is able to quantitatively improve registration quality. In addition, we provide a case study that demonstrates how the proposed method for cross-slide WSI registration could be used as part of a specific co-expression analysis framework

A fast method for approximate registration of whole-slide images of serial sections using local curvature

We present a method for fast, approximate registration of whole-slide images (WSIs) of histopathology serial sections. Popular histopathology slide registration methods in the existing literature tend towards intensity-based approaches.1, 2 Further input, in the form of an approximate initial transformation to be applied to one of the two WSIs, is then usually required, and this transformation needs to be optimised. Such a transformation is not readily available in this context and thus there is a need for fast approximation of these parameters. Fast registration is achieved by comparison of the external boundaries of adjacent tissue sections, using local curvature on multiple scales to assess similarity. Our representation of curvature is a modified version of the Curvature Scale Space (CSS)3 image. We substitute zero crossings with signed local absolute maxima of curvature to improve the registration's robustness to the subtle morphological differences of adjacent sections. A pairwise matching is made between curvature maxima at scales increasing exponentially, the matching minimizes the distance between maxima pairs at each scale. The boundary points corresponding to the matched maxima pairs are used to estimate the desired transformation. Our method is highly robust to translation, rotation, and linear scaling, and shows good performance in cases of moderate non-linear scaling. On our set of test images the algorithm shows improved reliability and processing speed in comparison to existing CSS based registration methods.Scopu

Block diagram of the proposed method.

<p>Block diagram of the proposed method.</p

Results of nuclei detection algorithm in [2, 28] trained and tested for different stain deconvolution algorithms.

<p>Values show the mean and standard deviation for each of the precision, recall, and F1 score measures. Note that the evaluated algorithms are dynamically estimating stain colour based on current information. Thus, consistency of the algorithm could improve the detection accuracy. However, we did not include stain normalization in this experiments to avoid affecting the deconvolution results.</p

Blind source separation using sub-band decomposition with ICA.

<p>A filtering operator is applied to the observed signal <i>x</i>_<i>i</i> to extract independent and dependent sub-bands. The unmixing matrix is then estimated using a selected set of sub-bands which maximises independency among sources.</p

Correlation between the density maps and the ground truth.

<p>Indices a, b, c, d, and e of the x-axis show the correlation results for the Proposed method, Macenko <i>et al.</i> [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref011" target="_blank">11</a>], Ruifrok and Johnston [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref010" target="_blank">10</a>], BCD [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref013" target="_blank">13</a>],and ICA [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169875#pone.0169875.ref012" target="_blank">12</a>], respectively. Due to the high difference in the correlation margin between ICA and the other algorithms in the H density estimation for the second dataset, ICA has been removed in order to make the correlations of the other algorithms noticeable.</p