Automated robust registration of grossly misregistered whole-slide images with varying stains

Litjens, Geert; Safferling, Kai; Grabe, Niels

doi:10.1117/12.2216672

Cited by 4 publications

(6 citation statements)

References 9 publications

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“…Currently, there are a number of WSI-compatible tools available for utilization including OpenHI, ASAP, QuPath, Cytomine, OpenSeadragon, and SlideJ. [ 12 13 28 29 30 31 ] These frameworks are successful implementations of computational pathology to support visualization, annotation, and further pathological analysis. Unfortunately, many of them were being forced to only support proprietary WSI file formats since the free and open ones are not available.…”

Section: Discussionmentioning

confidence: 99%

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LibMI: An Open Source Library for Efficient Histopathological Image Processing

Dong

Puttapirat

Deng

et al. 2020

Journal of Pathology Informatics

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Background: Whole-slide images (WSIs) as a kind of image data are rapidly growing in the digital pathology domain. With unusual high resolution, these images make them hard to be supported by conventional tools or file formats. Thus, it obstructs data sharing and automated analysis. Here, we propose a library, LibMI, along with its open and standardized image file format. They can be used together to efficiently read, write, modify, and annotate large images. Materials and Methods: LibMI utilizes the concept of pyramid image structure and lazy propagation from a segment tree algorithm to support reading and modifying and to guarantee that both operations have linear time complexity. Further, a cache mechanism was introduced to speed up the program. Results: LibMI is an open and efficient library for histopathological image processing. To demonstrate its functions, we applied it to several tasks including image thresholding, microscopic color correction, and storing pixel-wise information on WSIs. The result shows that libMI is particularly suitable for modifying large images. Furthermore, compared with congeneric libraries and file formats, libMI and modifiable multiscale image (MMSI) run 18.237 times faster on read-only tasks. Conclusions: The combination of libMI library and MMSI file format enables developers to efficiently read and modify WSIs, thus can assist in pixel-wise image processing on extremely large images to promote building image processing pipeline. The library together with the data schema is freely available on GitLab: https://gitlab.com/BioAI/libMI .

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Section: Discussionmentioning

confidence: 99%

“…Third, the ad hoc solutions cannot support easy reuse of existing annotations and the interoperation between analysis software. [ 12 13 ] Large image processing lacks an universal and comprehensive solution.…”

Section: Introductionmentioning

confidence: 99%

LibMI: An Open Source Library for Efficient Histopathological Image Processing

Dong

Puttapirat

Deng

et al. 2020

Journal of Pathology Informatics

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“…We used an end‐to‐end CNN model to label a given image patch into one of the six classes: stroma, lymphocytes, necrosis, tumour, normal lobules, and other (including miscellaneous other tissue types and the fatty tissue), employing the ResNet neural network model [32] to perform initial classification of the tissue types. An expert pathologist (JA) manually annotated regions corresponding to the six tissue types (stroma, lymphocytes, necrosis, tumour, normal lobules, and others) using the Automated Slide Analysis Platform (ASAP) annotation tool [33] for training of the tissue classification algorithm. The batch size was set to 16 and the network was trained for 100 epochs with a learning rate of 0.001.…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence‐based digital scores of stromal tumour‐infiltrating lymphocytes and tumour‐associated stroma predict disease‐specific survival in triple‐negative breast cancer

Albusayli

Graham

Pathmanathan

et al. 2023

The Journal of Pathology

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Triple‐negative breast cancer (TNBC) is known to have a relatively poor outcome with variable prognoses, raising the need for more informative risk stratification. We investigated a set of digital, artificial intelligence (AI)‐based spatial tumour microenvironment (sTME) features and explored their prognostic value in TNBC. After performing tissue classification on digitised haematoxylin and eosin (H&E) slides of TNBC cases, we employed a deep learning‐based algorithm to segment tissue regions into tumour, stroma, and lymphocytes in order to compute quantitative features concerning the spatial relationship of tumour with lymphocytes and stroma. The prognostic value of the digital features was explored using survival analysis with Cox proportional hazard models in a cross‐validation setting on two independent international multi‐centric TNBC cohorts: The Australian Breast Cancer Tissue Bank (AUBC) cohort (n = 318) and The Cancer Genome Atlas Breast Cancer (TCGA) cohort (n = 111). The proposed digital stromal tumour‐infiltrating lymphocytes (Digi‐sTILs) score and the digital tumour‐associated stroma (Digi‐TAS) score were found to carry strong prognostic value for disease‐specific survival, with the Digi‐sTILs and Digi‐TAS scores giving C‐index values of 0.65 (p = 0.0189) and 0.60 (p = 0.0437), respectively, on the TCGA cohort as a validation set. Combining the Digi‐sTILs feature with the patient's positivity status for axillary lymph nodes yielded a C‐index of 0.76 on unseen validation cohorts. We surmise that the proposed digital features could potentially be used for better risk stratification and management of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

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“…The WSI were checked in the automated slide analysis platform ASAP 1 [13] and all biopsy cores containing carcinoma were manually annotated in ASAP for further analysis. Annotations were performed as polygons, containing carcinoma areas.…”

Section: Methodsmentioning

confidence: 99%

“…The data set is available as raw files stored in Mirax MRXS format 2 compatible with the OpenSlide library [14]. Annotations used for the evaluation stage are available as XML files compatible with ASAP [13]. The data set is pseudonymized and access can be requested via BBMRI-ERIC European Research Infrastructure by following its access policy 3 ; the request should be placed via BBMRI-ERIC Negotiator platform 4 to Masaryk Memorial Cancer Institute.…”

Section: Methodsmentioning

confidence: 99%

Shedding Light on the Black Box of a Neural Network Used to Detect Prostate Cancer in Whole Slide Images by Occlusion-Based Explainability

Krajňanský

Gallo

Nenutil

et al. 2022

Preprint

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Diagnostic histopathology is facing increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure, with promising results for many routine diagnostic procedures. However, one major issue with deep learning approaches is their lack of interpretability - after adequate training they perform their assigned tasks admirably, but do not explain how they reach their conclusions. Knowledge of how a given method performs its task with high sensitivity and specificity would be advantageous to understand the key features responsible for diagnosis, and should in turn allow fine-tuning of deep learning approaches. This paper presents a deep learning-based system for carcinoma detection in whole slide images of prostate core biopsies, achieving state-of-the-art performance; 100% area under curve and sensitivity of 0.978 for 8 detected false positives on average per slide. Furthermore, we investigated various methods to extract the key features used by the neural network for classification. Of these, the technique called occlusion, adapted to whole slide images, analyzes the sensitivity of the detection system to changes in the input images. This technique produces heatmaps indicating which parts of the image have the strongest impact on the system's output that a histopathologist can examine to identify the network's reasoning behind a given classification. Reassuringly, the heatmaps identified several prevailing histomorphological features characterizing carcinoma, e.g. single-layered epithelium, presence of small lumina, and hyperchromatic nuclei with halos. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The results show that the neural network approach to recognize prostatic cancer is similar to that taken by a human pathologist at medium optical resolution. The use of explainability heatmaps provides added value for automated digital pathology to analyze and fine-tune deep learning systems, and improves trust in computer-based decisions.

show abstract

Automated robust registration of grossly misregistered whole-slide images with varying stains

Cited by 4 publications

References 9 publications

LibMI: An Open Source Library for Efficient Histopathological Image Processing

LibMI: An Open Source Library for Efficient Histopathological Image Processing

Artificial intelligence‐based digital scores of stromal tumour‐infiltrating lymphocytes and tumour‐associated stroma predict disease‐specific survival in triple‐negative breast cancer

Shedding Light on the Black Box of a Neural Network Used to Detect Prostate Cancer in Whole Slide Images by Occlusion-Based Explainability

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