34 research outputs found
Registration and multi-immunohistochemical analysis of whole slide images of serial tissue sections.
No full textThe 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
Computational pathology applied to clinical colorectal cancer cohorts identifies immune and endothelial cell spatial patterns predictive of outcome
No full textColorectal cancer (CRC) is a histologically heterogeneous disease with variable clinical outcome. The role the tumour microenvironment (TME) plays in determining tumour progression is complex and not fully understood. To improve our understanding, it is critical that the TME is studied systematically within clinically annotated patient cohorts with long-term follow-up. Here we studied the TME in three clinical cohorts of metastatic CRC with diverse molecular subtype and treatment history. The MISSONI cohort included cases with microsatellite instability that received immunotherapy (n = 59, 24 months median follow-up). The BRAF cohort included BRAF V600E mutant microsatellite stable (MSS) cancers (n = 141, 24 months median follow-up). The VALENTINO cohort included RAS/RAF WT MSS cases who received chemotherapy and anti-EGFR therapy (n = 175, 32 months median follow-up). Using a Deep learning cell classifier, trained upon >38,000 pathologist annotations, to detect eight cell types within H&E-stained sections of CRC, we quantified the spatial tissue organisation and colocalisation of cell types across these cohorts. We found that the ratio of infiltrating endothelial cells to cancer cells, a possible marker of vascular invasion, was an independent predictor of progression-free survival (PFS) in the BRAF+MISSONI cohort (p = 0.033, HR = 1.44, CI = 1.029–2.01). In the VALENTINO cohort, this pattern was also an independent PFS predictor in TP53 mutant patients (p = 0.009, HR = 0.59, CI = 0.40–0.88). Tumour-infiltrating lymphocytes were an independent predictor of PFS in BRAF+MISSONI (p = 0.016, HR = 0.36, CI = 0.153–0.83). Elevated tumour-infiltrating macrophages were predictive of improved PFS in the MISSONI cohort (p = 0.031). We validated our cell classification using highly multiplexed immunofluorescence for 17 markers applied to the same sections that were analysed by the classifier (n = 26 cases). These findings uncovered important microenvironmental factors that underpin treatment response across and within CRC molecular subtypes, while providing an atlas of the distribution of 180 million cells in 375 clinically annotated CRC patients. © 2025 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland
Hyper-stain inspector : a framework for robust registration and localised co-expression analysis of multiple whole-slide images of serial histology sections
No full textIn 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
No full textWe 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
Stain deconvolution using statistical analysis of multi-resolution stain colour representation
No full textStain 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
No full textAims:
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
Bland Altman plot for the proposed method (left) and ICA [12] (right) for H and E stains using all datasets.
No full textSame randomly selected pixels are plotted from all three datasets by running the proposed method and ICA [12]. Median of agreement is -0.002 for the proposed method and -0.005 for [12]. Limits of agreements for the proposed method is [-0.48, 48] compared to [-0.79, 0.78] for ICA [12].</p
Correlation between the density maps and the ground truth.
No full textIndices a, b, c, d, and e of the x-axis show the correlation results for the Proposed method, Macenko et al. [11], Ruifrok and Johnston [10], BCD [13],and ICA [12], 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
Euclidean Distance between the estimated stain matrix and the ground truth.
No full textThe median of the Euclidean distances for each method is shown in the last two columns. Last row shows the median of the Euclidean distances for all methods to highlight the significance of the best achieved median values.</p
