histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing

Marcolini, Alessia; Bussola, Nicole; Arbitrio, Ernesto; Amgad, Mohamed; Jurman, Giuseppe; Furlanello, Cesare

doi:10.1016/j.softx.2022.101237

Cited by 18 publications

(17 citation statements)

References 31 publications

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“…In our study, we employed the Histolab 1 library (43) to facilitate the tiling process of the slides. This library offers tools to generate tissue masks and perform the tiling process (43).…”

Section: Methodsmentioning

confidence: 99%

Generating synthetic data in digital pathology through diffusion models: a multifaceted approach to evaluation

Pozzi,

Noei,

Robbi

et al. 2023

Preprint

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Synthetic data has recently risen as a new precious item in the computational pathologist’s toolbox, supporting several tasks such as helping with data scarcity or augmenting training set in deep learning. Nonetheless, the use of such novel resources requires a carefully planned construction and evaluation, to avoid pitfalls such as the generation of clinically meaningless artifacts.As the major outcome described in the current manuscript, a novel full stack pipeline is introduced for the generation and evaluation of synthetic pathology data powered by a diffusion model. The workflow features, as characterizing elements, a new multifaceted evaluation strategy with an embedded explainability procedure effectively tackling two critical aspects of the use of synthetic data in health-related domains.An ensemble-like strategy is adopted for the evaluation of the produced data, with the threefold aim of assessing the similarity of real and synthetic data through a set of well-established metrics, evaluating the practical usability of the generated images in deep learning models complemented by explainable AI methods, and validating their histopathological realism through a dedicated questionnaire answered by three professional pathologists.The pipeline is demonstrated on the public GTEx dataset of 650 WSIs, including five different tissues, conditioning the training step of the underlying diffusion model. An equal number of tiles from each of these five tissues are then generated. Finally, the reliability of the generated data is assessed using the proposed evaluation pipeline, with encouraging results. We show that each of these evaluation steps are necessary as they provide complementary information on the generated data’s quality.Overall, all the aforementioned features characterize the proposed workflow as a fully-fledged solution for generative AI in digital pathology representing a potentially useful tool for the digital pathology community in their transition towards digitalization and data-driven modeling.

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Section: Methodsmentioning

confidence: 99%

Generating synthetic data in digital pathology through diffusion models: a multifaceted approach to evaluation

Pozzi,

Noei,

Robbi

et al. 2023

Preprint

Self Cite

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show abstract

“…Tiling the slide and scoring tiles at a very low resolution (2 MPP); 4. Analyzing the top 300 tiles [22,23]. Fixing the number of analyzed ROIs ensured a near-constant run time of less than two hours per slide.…”

Section: Methodsmentioning

confidence: 99%

A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the MuTILs model and PanopTILs dataset

Amgad

Salgado

Cooper

2022

Preprint

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Tumor-Infiltrating Lymphocytes (TILs) have strong prognostic and predictive value in breast cancer, but their visual assessment is subjective. We present MuTILs, a convolutional neural network architecture specifically optimized for the assessment of TILs in whole-slide image scans in accordance with clinical scoring recommendations. MuTILs is a concept bottleneck model, designed to be explainable and to encourage sensible predictions at multiple resolutions. Our computational scores match visual scores and have independent prognostic value in invasive breast cancers from the TCGA dataset.

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“…Grid tiling, which WSI is partitioned into multiple non-overlapping tile images in a grid-like pattern from top left corner of the slide image, is the most common tiling strategy and it's well understood. Tools such as Histolab [17], PyHIST [18], and SliDL [19] are capable of performing holistic WSI grid tiling strategy.…”

Section: Introductionmentioning

confidence: 99%

PathEX: Make Good Choice for Whole Slide Image Extraction

Yang,

Zhang,

Yang

et al. 2024

Preprint

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BackgroundThe tile-based approach has been widely used for slide-level predictions in whole slide image (WSI) analysis. However, the irregular shapes and variable dimensions of tumor regions pose challenges for the process. To address this issue, we proposed PathEX, a framework that integrates intersection over tile (IoT) and background over tile (BoT) algorithms to extract tile images around boundaries of annotated regions while excluding the blank tile images within these regions.MethodsWe developed PathEX, which incorporated IoT and BoT into tile extraction, for training a classification model in CAM (239 WSIs) and PAIP (40 WSIs) datasets. By adjusting the IoT and BoT parameters, we generated eight training sets and corresponding models for each dataset. The performance of PathEX was assessed on the testing set comprising 13,076 tile images from 48 WSIs of CAM dataset and 6,391 tile images from 10 WSIs of PAIP dataset.ResultsPathEX could extract tile images around boundaries of annotated region differently by adjusting the IoT parameter, while exclusion of blank tile images within annotated regions achieved by setting the BoT parameter. As adjusting IoT from 0.1 to 1.0, and 1 - BoT from 0.0 to 0.5, we got 8 train sets. Experimentation revealed that set C demonstrates potential as the most optimal candidate. Nevertheless, a combination of IoT values ranging from 0.2 to 0.5 and 1-BoT values ranging from 0.2 to 0.5 also yielded favorable outcomes.ConclusionsIn this study, we proposed PathEX, a framework that integrates IoT and BoT algorithms for tile image extraction at the boundaries of annotated regions while excluding blank tiles within these regions. Researchers can conveniently set the thresholds for IoT and BoT to facilitate tile image extraction in their own studies. The insights gained from this research provide valuable guidance for tile image extraction in digital pathology applications.

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histolab: A Python library for reproducible Digital Pathology preprocessing with automated testing

Cited by 18 publications

References 31 publications

Generating synthetic data in digital pathology through diffusion models: a multifaceted approach to evaluation

Generating synthetic data in digital pathology through diffusion models: a multifaceted approach to evaluation

A panoptic segmentation approach for tumor-infiltrating lymphocyte assessment: development of the MuTILs model and PanopTILs dataset

PathEX: Make Good Choice for Whole Slide Image Extraction

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