title: DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks.
abstract: In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. It extends the approach of the well-known GrabCut [1] method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naïve approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.
authors.forename: Martin
authors.initials: M
authors.lastname: Rajchl
authors.forename: Matthew C H
authors.initials: MC
authors.lastname: Lee
authors.forename: Ozan
authors.initials: O
authors.lastname: Oktay
authors.forename: Konstantinos
authors.initials: K
authors.lastname: Kamnitsas
authors.forename: Jonathan
authors.initials: J
authors.lastname: Passerat-Palmbach
authors.forename: Wenjia
authors.initials: W
authors.lastname: Bai
authors.forename: Mellisa
authors.initials: M
authors.lastname: Damodaram
authors.forename: Mary A
authors.initials: MA
authors.lastname: Rutherford
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
authors.forename: Bernhard
authors.initials: B
authors.lastname: Kainz
authors.forename: Daniel
authors.initials: D
authors.lastname: Rueckert
mesh_terms: D000465:Algorithms; D001921:Brain; D006801:Humans; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging; D009010:Monte Carlo Method; D016571:Neural Networks, Computer