Abstract
Algorithms proposed in computational pathology can allow to automatically analyze digitized tissue samples of histopathological images to help diagnosing diseases. Tissue samples are scanned at a high-resolution and usually saved as images with several magnification levels, namely whole slide images (WSIs). Convolutional neural networks (CNNs) represent the state-of-the-art computer vision methods targeting the analysis of histopathology images, aiming for detection, classification and segmentation. However, the development of CNNs that work with multi-scale images such as WSIs is still an open challenge. The image characteristics and the CNN properties impose architecture designs that are not trivial. Therefore, single scale CNN architectures are still often used. This paper presents Multi_Scale_Tools, a library aiming to facilitate exploiting the multi-scale structure of WSIs. Multi_Scale_Tools currently include four components: a pre-processing component, a scale detector, a multi-scale CNN for classification and a multi-scale CNN for segmentation of the images. The pre-processing component includes methods to extract patches at several magnification levels. The scale detector allows to identify the magnification level of images that do not contain this information, such as images from the scientific literature. The multi-scale CNNs are trained combining features and predictions that originate from different magnification levels. The components are developed using private datasets, including colon and breast cancer tissue samples. They are tested on private and public external data sources, such as The Cancer Genome Atlas (TCGA). The results of the library demonstrate its effectiveness and applicability. The scale detector accurately predicts multiple levels of image magnification and generalizes well to independent external data. The multi-scale CNNs outperform the single-magnification CNN for both classification and segmentation tasks. The code is developed in Python and it will be made publicly available upon publication. It aims to be easy to use and easy to be improved with additional functions.
Highlights
The implicit multi-scale structure of digitized histopathological images represents an open challenge in computational pathology
Multi_Scale_Tools library aims at facilitating the exploitation of multi-scale structure in Whole Slide Images (WSIs) with code that is easy to use and easy to be improved with additional functions
Two multi-scale Convolutional neural networks (CNNs) architectures are developed for fully-supervised classification
Summary
The implicit multi-scale structure of digitized histopathological images represents an open challenge in computational pathology. Whole Slide Images (WSIs) are digitized histopathology images that are scanned at high-resolution and are stored in a multi-scale (pyramidal) format. The de facto standard spatial resolutions adopted to scan tissue samples (for example in The Cancer Genome Atlas) are usually 0.23–0.25 μm (magnification ×40) or 0.46–0.50 μm (magnification ×20). Tissue samples such as surgical resection samples (or specimens) are often approximately 20 mm × 15 mm in size, while samples such as biopsies are approximatively 2 mm × 6 mm in size. The multi-scale WSI format (Figure 1) includes several magnification levels (with a different spatial resolution) of the sample, stored in a pyramid, usually varying between ×1.25 and 40x.
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