Instance segmentation of biomedical images with an object-aware embedding learned with local constraints
Chen L, Strauch M, Merhof D (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11764 LNCS
Pages Range: 451-459
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030322380
DOI: 10.1007/978-3-030-32239-7_50
Abstract
Automatic instance segmentation is a problem that occurs in many biomedical applications. State-of-the-art approaches either perform semantic segmentation or refine object bounding boxes obtained from detection methods. Both suffer from crowded objects to varying degrees, merging adjacent objects or suppressing a valid object. In this work, we assign an embedding vector to each pixel through a deep neural network. The network is trained to output embedding vectors of similar directions for pixels from the same object, while adjacent objects are orthogonal in the embedding space, which effectively avoids the fusion of objects in a crowd. Our method yields state-of-the-art results even with a light-weighted backbone network on a cell segmentation (BBBC006 + DSB2018) and a leaf segmentation data set (CVPPP2017). The code and model weights are public available (https://github.com/looooongChen/instance_segmentation_with_pixel_embeddings/).
Involved external institutions
Germany (DE)How to cite
APA:
Chen, L., Strauch, M., & Merhof, D. (2019). Instance segmentation of biomedical images with an object-aware embedding learned with local constraints. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 451-459). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Chen, Long, Martin Strauch, and Dorit Merhof. "Instance segmentation of biomedical images with an object-aware embedding learned with local constraints." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 451-459.
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