Human Blastocyst's Zona Pellucida Segmentation via Boosting Ensemble of Complementary Learning

Peer reviewed: 
Yes, item is peer reviewed.
Scholarly level: 
Graduate student (PhD)
Final version published as: 

Moradi Rad, Reza & Saeedi, Parvaneh & Au, Jason & Havelock, Jon. (2018). Human Blastocyst's Zona Pellucida segmentation via boosting ensemble of complementary learning. Informatics in Medicine Unlocked. 13. DOI: 10.1016/j.imu.2018.10.009.

Date created: 
2018-10-25
Keywords: 
Zona pellucida
Human embryo
IVF
Medical image analysis
Deep neural network
Abstract: 

Characteristics of Zona Pellucida (ZP), particularly its thickness, is a key indicator of human blastocyst (day-5embryo) quality. Therefore, ZP segmentation is an important step towards achieving automatic embryo qualityassessment. In this paper, a novel approach based on boosting ensemble of hybrid complementary learning isproposed to segment Zona Pellucida in human blastocyst images. First, an inner-ZP localization method isproposed to separate the ZP from the heavily textured area inside a blastocyst. Then, a deep Hierarchical NeuralNetwork (HiNN) is proposed to segment ZP area. The hierarchical nature of the proposed network enableslearning features with respect to their spatial location in the embryo. Finally, a Self-supervised Image-SpecificRefinement (SISR) strategy is proposed as a complementary step to boost the performance. The proposed systemis a hybrid approach in the sense that the HiNN learns the intra-correlation among images, while the SISR takesinto account the inter-correlation within the query image. Experimental results confirm that the proposed method is capable of identifying ZP area with average Precision, Recall, Accuracy and Jaccard Index of 85.2%, 92.0%, 95.6% and 78.1%, respectively. The proposed HiNN system outperforms state of the art by 4.9% in Precision, 11.2% in Recall, 3.6% in Accuracy and 10.7% in Jaccard Index.

Language: 
English
Document type: 
Article
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