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Abstract
Different to semantic segmentation, instance segmentation assigns unique labels to each individual instance of the same class. In this work, we propose a novel recurrent fully convolutional network architecture for tracking such instance segmentations over time. The network architecture incorporates convolutional gated recurrent units (ConvGRU) into a stacked hourglass network to utilize temporal video information. Furthermore, we train the network with a novel embedding loss based on cosine similarities, such that the network predicts unique embeddings for every instance throughout videos. Afterwards, these embeddings are clustered among subsequent video frames to create the final tracked instance segmentations. We evaluate the recurrent hourglass network by segmenting left ventricles in MR videos of the heart, where it outperforms a network that does not incorporate video information. Furthermore, we show applicability of the cosine embedding loss for segmenting leaf instances on still images of plants. Finally, we evaluate the framework for instance segmentation and tracking on six datasets of the ISBI celltracking challenge, where it shows state-of-the-art performance.
Original language | English |
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Title of host publication | Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings |
Publisher | Springer Verlag Heidelberg |
Pages | 3-11 |
Number of pages | 9 |
ISBN (Print) | 9783030009335 |
DOIs | |
Publication status | Published - 16 Sept 2018 |
Event | 21st International Conference on Medical Image Computing and Computer Assisted Intervention: MICCAI 2018 - Granada, Spain Duration: 16 Sept 2018 → 20 Sept 2018 |
Publication series
Name | Lecture Notes in Computer Science |
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Volume | 11071 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 21st International Conference on Medical Image Computing and Computer Assisted Intervention |
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Country/Territory | Spain |
City | Granada |
Period | 16/09/18 → 20/09/18 |
Keywords
- Cell
- Embeddings
- Instances
- Recurrent
- Segmentation
- Tracking
- Video
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science
Fields of Expertise
- Information, Communication & Computing
Cooperations
- BioTechMed-Graz
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Dive into the research topics of 'Instance segmentation and tracking with cosine embeddings and recurrent hourglass networks'. Together they form a unique fingerprint.Projects
- 1 Finished
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FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents
Bischof, H. (Co-Investigator (CoI)) & Urschler, M. (Co-Investigator (CoI))
1/07/15 → 31/12/18
Project: Research project