CTU Events

Tammy R. Raviv: Diving Deep into Cell Segmentation in Microscopy Videos

31 May 2019   11:00-12:30

The analysis of cluttered objects in a video sequence is a challenging task, particularly in the presence of complex spatial structures and complicated temporal changes. We present a Deep Neural Network framework which addresses two aspects of object segmentation within video sequences, namely, the inherent dependencies between video frames and the evaluation of segmentation results. We propose the integration of the U-Net architecture (Ronneberger et. al) with Convolutional Long Short Term Memory (C-LSTM). The segmentation network's unique architecture enables it to capture multi-scale, compact, spatio-temporal encoding of the objects in the C-LSTMs memory units. The proposed network exploits temporal cues which facilitate the individual segmentation of touching or partially occluded objects. The method was applied to live cell microscopy data and tested on the common cell segmentation benchmark, the Cell Tracking Challenge (www.celltrackingchallenge.net), and ranked 1st and 2nd place on two challenging datasets. We further present a novel method for the quality assurance (QA) of segmentation methods, the QANet. The network, based on our novel RibCage architecture, estimates the Intersection over Union of a proposed segmentation without the need for ground truth annotations. The Rib-Cage network is suited for this task since it is designed such that multi-level features of both the image and segmentation maps are compared at multiple scales allowing for the extraction of complex joint representations.

Place

KN:G-205, FEL ČVUT, Karlovo náměstí 13, Praha 2

Organizer

Katedra kybernetiky FEL ČVUT

Contact person

prof. Dr. Ing. Jan Kybic, jan.kybic@fel.cvut.cz

More information

https://cyber.felk.cvut.cz/seminars/?event=1312