Dataset Information

AutoTag and AutoSnap: Standardized, semi-automatic capture of regions of interest from whole slide images.

ABSTRACT: Tumor angiogenesis is measured by counting microvessels in tissue sections at high power magnification as a potential prognostic or predictive biomarker. Until now, regions of interest (ROIs) were selected by manual operations within a tumor by using a systematic uniform random sampling (SURS) approach. Although SURS is the most reliable sampling method, it implies a high workload. However, SURS can be semi-automated and in this way contribute to the development of a validated quantification method for microvessel counting in the clinical setting. Here, we report a method to use semi-automated SURS for microvessel counting: •Whole slide imaging with Pannoramic SCAN (3DHISTECH)•Computer-assisted sampling in Pannoramic Viewer (3DHISTECH) extended by two self-written AutoHotkey applications (AutoTag and AutoSnap)•The use of digital grids in Photoshop(®) and Bridge(®) (Adobe Systems) This rapid procedure allows traceability essential for high throughput protein analysis of immunohistochemically stained tissue.

SUBMITTER: Marien KM

PROVIDER: S-EPMC4487922 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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AutoTag and AutoSnap: Standardized, semi-automatic capture of regions of interest from whole slide images.

Marien Koen M KM Andries Luc L De Schepper Stefanie S Kockx Mark M MM De Meyer Guido R Y GR

MethodsX 20150521

Tumor angiogenesis is measured by counting microvessels in tissue sections at high power magnification as a potential prognostic or predictive biomarker. Until now, regions of interest (ROIs) were selected by manual operations within a tumor by using a systematic uniform random sampling (SURS) approach. Although SURS is the most reliable sampling method, it implies a high workload. However, SURS can be semi-automated and in this way contribute to the development of a validated quantification met ...[more]

PMID: 26150998

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Project description:Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as “test reference dataset” and “test target dataset”) and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1% and 96.3%, 95% and 87.8%, and 73.9% and 70.6%, respectively. At slide level, accuracy, recall, and precision were 97.6% and 92.0%, 90.9% and 100%, and 100% and 70.8% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice. Author summary Recent developments in digital slide scanner technologies and the need for optimizing histopathologists workflow have led to the rise of digital pathology. Along with digital pathology, artificial intelligence assisted diagnoses are foreseen as the way to further enhance pathologists’ productivity and therefore support them with their ever-growing workload. Although imaging technologies can be digitalized, most of the slide preparation is still a manual process which lacks any sort of standardization. For this reason, digital images appearance is dependent on the center where the slide is prepared and artificial intelligence algorithms will perform poorly on slides from previously unseen medical centers. In this work, we introduce a method, based on transfer learning, for adapting artificial intelligence algorithms to new medical centers. We show that the method allows for algorithm performance preservation with reduced need for training data.

Dataset Information

AutoTag and AutoSnap: Standardized, semi-automatic capture of regions of interest from whole slide images.

Publications

AutoTag and AutoSnap: Standardized, semi-automatic capture of regions of interest from whole slide images.

Similar Datasets

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