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Generating annotation data of tissue images

A technology for image data and organizing images, applied in the field of computer data structure, can solve the time-consuming problems of cell objects

Pending Publication Date: 2021-04-23
KONINKLJIJKE PHILIPS NV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Although this may be feasible for larger regions of pathology images, such as tissue types, annotating a large number of cellular objects is time-consuming since typically thousands of objects need to be annotated

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  • Generating annotation data of tissue images
  • Generating annotation data of tissue images
  • Generating annotation data of tissue images

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Embodiment Construction

[0082] Building large annotated datasets for training and validating image analysis algorithms used in computational pathology is an important task. For deep learning, the availability of large annotated datasets, which can involve hundreds or thousands of pathology slides, is important for the success of such algorithmic halting in this case. Applying computational recognition processes to such large image datasets may involve excessive computer time. Such large datasets would be difficult to accurately manipulate using human annotators, where individual units in the dataset must be identified and categorized (annotated).

[0083] Therefore, it has been proposed to annotate large datasets using biomarker staining. In this approach, biomarkers that specifically bind to the cell or tissue type that need to be annotated are used. Image data of tissues stained by analyzing such biomarkers, and annotation masks (annotation data) can be efficiently created by computer processing ...

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Abstract

Currently, there is interest in applying machine learning techniques to analyse digital pathology images automatically. Machine learning techniques often rely on training with a ground-truth image input. The quality and amount of training data determines the quality of the detector, as expressed in the rate of true and false positives, and robustness against variations in the appearance of the input images. The present application proposes to obtain image data of the same sample before and after at least one re- staining step (firstly with a structure-revealing stain, and secondly with a bio marker revealing stain). Sections of the first and second image data having a good registration relationship are chosen, along with the probability of detecting a desired candidate object (such as nucleus) and the probability of the bio marker revealing stain being present annotation data suitable for training a machine learning algorithm on the first and / or the second image data is provided.

Description

technical field [0001] The present invention relates to apparatus for automatically generating annotation data of tissue images, and related computer-implemented medical image annotation methods, methods for generating annotation data for tissue images, methods for training machine learning models, and computer programs Units, computer readable media, and computer data structures. Background technique [0002] Currently, there is interest in applying machine learning techniques to automatically analyze digital pathology images. Machine learning techniques often rely on training with real-world imagery input. The quality and amount of training data determine the quality of the detector as expressed by the ratio of true and false positives, as well as its robustness to changes in the appearance of the input image. [0003] Ground-truth training images (or image patches) are typically provided from manually annotated images annotated by pathologists. While this may be feasib...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/30024G06T7/11G01N1/30G01N1/34G06T11/00G06T2207/10056G06T2207/20072G06T2207/20081G06T2207/30168
Inventor 左菲A·派里克R·温贝格尔-弗里德尔K·德拉特M·范德里
Owner KONINKLJIJKE PHILIPS NV