Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
- Authors
- Wong, A.; Chen, A.; Wu, Y.; Cicek, S.; Tiard, A.; Hong, Byung-Woo; Soatto, S.
- Issue Date
- 2022
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.12962 LNCS, pp 75 - 87
- Pages
- 13
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 12962 LNCS
- Start Page
- 75
- End Page
- 87
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/60333
- DOI
- 10.1007/978-3-031-08999-2_6
- ISSN
- 0302-9743
1611-3349
- Abstract
- We present a method to segment MRI scans of the human brain into ischemic stroke lesion and normal tissues. We propose a neural network architecture in the form of a standard encoder-decoder where predictions are guided by a spatial expansion embedding network. Our embedding network learns features that can resolve detailed structures in the brain without the need for high-resolution training images, which are often unavailable and expensive to acquire. Alternatively, the encoder-decoder learns global structures by means of striding and max pooling. Our embedding network complements the encoder-decoder architecture by guiding the decoder with fine-grained details lost to spatial downsampling during the encoder stage. Unlike previous works, our decoder outputs at 2 × the input resolution, where a single pixel in the input resolution is predicted by four neighboring subpixels in our output. To obtain the output at the original scale, we propose a learnable downsampler (as opposed to hand-crafted ones e.g. bilinear) that combines subpixel predictions. Our approach improves the baseline architecture by ≈ 11.7% and achieves the state of the art on the ATLAS public benchmark dataset with a smaller memory footprint and faster runtime than the best competing method. Our source code has been made available at: https://github.com/alexklwong/subpixel-embedding-segmentation. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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