하이브리드 피처 생성 및 딥 러닝 기반 박테리아 세포의 세분화

• Published in: 멀티미디어학회논문지 Vol. 23; no. 8; pp. 965 - 976
• Main Authors: 임선자(Seon-Ja Lim), 칼렙부누누(Caleb Vununu), 권기룡(Ki-Ryong Kwon), 윤성대(Sung-Dae Youn)
• Format: Journal Article
• Language: Korean
• Published: 한국멀티미디어학회 2020
• Subjects: 전자/정보통신공학; Bacterial Cell Segmentation; Hybrid Feature; Bio-cell Informatics; Artificial Neural Network; Autoencoder; Deep Learning
• ISSN: 1229-7771; 2384-0102

We present in this work a segmentation method of E. coli bacterial images generated via phase contrast microscopy using a deep learning based hybrid feature generation. Unlike conventional machine learning methods that use the hand-crafted features, we adopt the denoising autoencoder in order to generate a precise and accurate representation of the pixels. We first construct a hybrid vector that combines original image, difference of Gaussians and image gradients. The created hybrid features are then given to a deep autoencoder that learns the pixels' internal dependencies and the cells' shape and boundary information. The latent representations learned by the autoencoder are used as the inputs of a softmax classification layer and the direct outputs from the classifier represent the coarse segmentation mask. Finally, the classifier's outputs are used as prior information for a graph partitioning based fine segmentation. We demonstrate that the proposed hybrid vector representation manages to preserve the global shape and boundary information of the cells, allowing to retrieve the majority of the cellular patterns without the need of any post-processing.