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The influence of image masks definition onsegmentation results of histopathological imagesusing convolutional neural network
Abstract
Abstract—In the era of collecting large amounts of tissue materials, assisting the work of histopathologists with various electronic and information IT tools is an undeniable fact. The traditional interaction between a human pathologist and the glass slide is changing to interaction between an AI pathologist with a whole slide images. One of the important tasks is the segmentation of objects (e.g. cells) in such images. In this study, weapplyU-netandV-netconvolutionalneuralnetworkmodelsto perform image segmentation. In particular, we analyze the role of the contour thickness in the reference (labels, masks) images on the results of image segmentation, also for the degraded images. We show the role of the proper mask definition and the results obtained for the ensemble models that use the same architecture but are trained using two sets of inverted masks.
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- Publication version
- Accepted or Published Version
- License
- Copyright (2019, IEEE)
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Details
- Category:
- Conference activity
- Type:
- publikacja w wydawnictwie zbiorowym recenzowanym (także w materiałach konferencyjnych)
- Language:
- English
- Publication year:
- 2019
- Bibliographic description:
- Jańczyk K., Neumann T., Rumiński J.: The influence of image masks definition onsegmentation results of histopathological imagesusing convolutional neural network// / : , 2019,
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/hsi47298.2019.8942600
- Verified by:
- Gdańsk University of Technology
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