Phase classification of mitotic events using selective dictionary learning for stem cell populations. (April 2018)

• Record Type: Journal Article
• Authors: Öztürk, Şaban; Akdemir, Bayram
• Is Part Of: Computers & electrical engineering. Volume 67(2018)
• Page Start: 25
• Page End: 37
• Publication Date: 2018-04
• Subjects: Cell division -- Dictionary learning -- Mitosis phase detection -- Selective dictionary; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.03.025
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A new dictionary learning structure which is specially designed for segmentation for mitosis phases. The difference between mitotic events and mitosis-like structures can be easily understood. Visualization of classification results. High success rate is achieved. Abstract: Nowadays, thanks to the use of advanced technological tools, stem cell studies which play an important role in regenerative medicine and cancer studies have increased considerably. In this study, selective dictionary learning method is presented for detecting mitotic event phases in stem cells using phase contrast time-lapse microscopy images. In the proposed method, three phases are defined for representation of mitotic events. Creating a dictionary that represents these phases with a single feature space restricts the success. For this reason, three dictionaries with different features are created. Although the multiplication of image alpha values with all generated dictionaries is quite suitable for determining the lowest error value, this process is time consuming. For this reason, a selective dictionary approach based on the automatic selection of the best values with a cooperation between the dictionaries has been proposed. In this way, the high success rate is maintained and the processing time is significantly reduced. The proposed method gives better results than other state-of-art studies in terms of computational efficiency and accuracy in experiments with C2C12 and BAEC datasets. Graphical abstract: Image, graphical abstract