title: Detecting cell division of Pseudomonas aeruginosa bacteria from bright-field microscopy images with hidden conditional random fields.
abstract: An approach to automatically detect bacteria division with temporal models is presented. To understand how bacteria migrate and proliferate to form complex multicellular behaviours such as biofilms, it is desirable to track individual bacteria and detect cell division events. Unlike eukaryotic cells, prokaryotic cells such as bacteria lack distinctive features, causing bacteria division difficult to detect in a single image frame. Furthermore, bacteria may detach, migrate close to other bacteria and may orientate themselves at an angle to the horizontal plane. Our system trains a hidden conditional random field (HCRF) model from tracked and aligned bacteria division sequences. The HCRF model classifies a set of image frames as division or otherwise. The performance of our HCRF model is compared with a Hidden Markov Model (HMM). The results show that a HCRF classifier outperforms a HMM classifier. From 2D bright field microscopy data, it is a challenge to separate individual bacteria and associate observations to tracks. Automatic detection of sequences with bacteria division will improve tracking accuracy.
authors.forename: Lee-Ling S
authors.initials: LS
authors.lastname: Ong
authors.lastname: Xinghua Zhang
authors.forename: Binu
authors.initials: B
authors.lastname: Kundukad
authors.forename: Justin
authors.initials: J
authors.lastname: Dauwels
authors.forename: Patrick
authors.initials: P
authors.lastname: Doyle
authors.forename: H Harry
authors.initials: HH
authors.lastname: Asada
mesh_terms: D000465:Algorithms; D002455:Cell Division; D007091:Image Processing, Computer-Assisted; D008390:Markov Chains; D008853:Microscopy / Q000379:methods*; D009068:Movement; D011550:Pseudomonas aeruginosa / Q000166:cytology*