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CNN Assisted Hybrid Algorithm for Medical Images Segmentationстатья Исследовательская статья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 10 февраля 2021 г.
- Авторы: Khvostikov Alexander, Krylov Andrey S., Mikhailov Ilya, Malkov Pavel
- Сборник: ICBIP '20: Proceedings of the 2020 5th International Conference on Biomedical Signal and Image Processing
- Год издания: 2020
- Издательство: ACM
- Местоположение издательства: New York, N.Y., United States
- Первая страница: 14
- Последняя страница: 19
- DOI: 10.1145/3417519.3417557
- Аннотация: In this report we focus on a hybrid method based on a convolutional neural network (CNN) for histological image segmentation. We propose a CNN assisted interactive segmentation tool with weakly-supervised learning to accelerate the process of manual image annotation. The core of our annotation approach is a classical KNN classifier that uses parameters predicted by CNN. User annotates an image with scribbles of two types corresponding to glands and non-glands histological structures. Next the model performs label propagation to all unlabeled pixels providing user a fully annotated image build from his scribbled-based input. User can interact with the annotation tool and add new scribbles to correct the result. The algorithm allows to reduce one image annotation time from 150 to 25-30 minutes for PATH-DT-MSU dataset that can potentially seriously increase the number of fully annotated histological images which is necessary for the development of diagnostic algorithms.
- Добавил в систему: Хвостиков Александр Владимирович