Small Lesion Segmentation in Brain MRIs with Subpixel Embedding
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Wong, A. | - |
dc.contributor.author | Chen, A. | - |
dc.contributor.author | Wu, Y. | - |
dc.contributor.author | Cicek, S. | - |
dc.contributor.author | Tiard, A. | - |
dc.contributor.author | Hong, Byung-Woo | - |
dc.contributor.author | Soatto, S. | - |
dc.date.accessioned | 2023-02-09T05:41:12Z | - |
dc.date.available | 2023-02-09T05:41:12Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.issn | 1611-3349 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/60333 | - |
dc.description.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. | - |
dc.format.extent | 13 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | Small Lesion Segmentation in Brain MRIs with Subpixel Embedding | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/978-3-031-08999-2_6 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.12962 LNCS, pp 75 - 87 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000878434800006 | - |
dc.identifier.scopusid | 2-s2.0-85135030798 | - |
dc.citation.endPage | 87 | - |
dc.citation.startPage | 75 | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 12962 LNCS | - |
dc.type.docType | Proceedings Paper | - |
dc.publisher.location | 미국 | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Radiology, Nuclear Medicine & Medical Imaging | - |
dc.description.journalRegisteredClass | scopus | - |
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