3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers
No Thumbnail Available
Access rights
openAccess
URL
Journal Title
Journal ISSN
Volume Title
A4 Artikkeli konferenssijulkaisussa
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2023
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
613-623
613-623
Series
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Volume 14227 LNCS
Abstract
Accurate 3D mitochondria instance segmentation in electron microscopy (EM) is a challenging problem and serves as a prerequisite to empirically analyze their distributions and morphology. Most existing approaches employ 3D convolutions to obtain representative features. However, these convolution-based approaches struggle to effectively capture long-range dependencies in the volume mitochondria data, due to their limited local receptive field. To address this, we propose a hybrid encoder-decoder framework based on a split spatio-temporal attention module that efficiently computes spatial and temporal self-attentions in parallel, which are later fused through a deformable convolution. Further, we introduce a semantic foreground-background adversarial loss during training that aids in delineating the region of mitochondria instances from the background clutter. Our extensive experiments on three benchmarks, Lucchi, MitoEM-R and MitoEM-H, reveal the benefits of the proposed contributions achieving state-of-the-art results on all three datasets. Our code and models are available at https://github.com/OmkarThawakar/STT-UNET.Description
Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Keywords
Electron Microscopy, Hybrid CNN-Transformers, Mitochondria instance segmentation, Spatio-Temporal Transformer
Other note
Citation
Thawakar, O, Anwer, R M, Laaksonen, J, Reiner, O, Shah, M & Khan, F S 2023, 3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers . in H Greenspan, H Greenspan, A Madabhushi, P Mousavi, S Salcudean, J Duncan, T Syeda-Mahmood & R Taylor (eds), Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 : Proceedings of 26th International Conference . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14227 LNCS, Springer, pp. 613-623, International Conference on Medical Image Computing and Computer Assisted Intervention, Vancouver, British Columbia, Canada, 08/10/2023 . https://doi.org/10.1007/978-3-031-43993-3_59