dc.contributor.author | Jha, Debesh | |
dc.contributor.author | Riegler, Michael Alexander | |
dc.contributor.author | Johansen, Dag | |
dc.contributor.author | Halvorsen, Pål | |
dc.contributor.author | Johansen, Håvard D. | |
dc.date.accessioned | 2021-01-29T13:54:04Z | |
dc.date.accessioned | 2021-03-05T14:38:41Z | |
dc.date.available | 2021-01-29T13:54:04Z | |
dc.date.available | 2021-03-05T14:38:41Z | |
dc.date.issued | 2020-09-01 | |
dc.identifier.citation | Jha, Riegler, Johansen, Halvorsen, Johansen. DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation. IEEE International Symposium on Computer-Based Medical Systems. 2020 | en |
dc.identifier.isbn | 978-1-7281-9429-5 | |
dc.identifier.isbn | 978-1-7281-9430-1 | |
dc.identifier.issn | 2372-9198 | |
dc.identifier.issn | 2372-918X | |
dc.identifier.uri | https://hdl.handle.net/10642/9895 | |
dc.description.abstract | Semantic image segmentation is the process of labeling each pixel of an image with its corresponding class. An encoder-decoder based approach, like U-Net and its variants, is a popular strategy for solving medical image segmentation tasks. To improve the performance of U-Net on various segmentation tasks, we propose a novel architecture called DoubleU-Net, which is a combination of two U-Net architectures stacked on top of each other. The first U-Net uses a pre-trained VGG-19 as the encoder, which has already learned features from ImageNet and can be transferred to another task easily. To capture more semantic information efficiently, we added another U-Net at the bottom. We also adopt Atrous Spatial Pyramid Pooling (ASPP) to capture contextual information within the network. We have evaluated DoubleU-Net using four medical segmentation datasets, covering various imaging modalities such as colonoscopy, dermoscopy, and microscopy. Experiments on the MICCAI 2015 segmentation challenge, the CVC-ClinicDB, the 2018 Data Science Bowl challenge, and the Lesion boundary segmentation datasets demonstrate that the DoubleU-Net outperforms U-Net and the baseline models. Moreover, DoubleU-Net produces more accurate segmentation masks, especially in the case of the CVC-ClinicDB and MICCAI 2015 segmentation challenge datasets, which have challenging images such as smaller and flat polyps. These results show the improvement over the existing U-Net model. The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models. | en |
dc.description.sponsorship | This work is funded in part by Research Council of Norway project number 263248 (Privaton). The computations in this paper were performed on equipment provided by the Experi- mental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under contract 270053. | en |
dc.language.iso | en | en |
dc.publisher | IEEE | en |
dc.relation.ispartof | 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) | |
dc.relation.ispartofseries | IEEE International Symposium on Computer-Based Medical Systems; 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS) | |
dc.rights | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en |
dc.subject | Image segmentation | en |
dc.subject | Decoding | en |
dc.subject | Task analyses | en |
dc.subject | Medical diagnostic imaging | en |
dc.subject | Semantics | en |
dc.subject | Lesions | en |
dc.title | DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation | en |
dc.type | Conference object | en |
dc.date.updated | 2021-01-29T13:54:04Z | |
dc.description.version | acceptedVersion | en |
dc.identifier.doi | https://doi.org/10.1109/CBMS49503.2020.00111 | |
dc.identifier.cristin | 1835631 | |
dc.source.journal | IEEE International Symposium on Computer-Based Medical Systems | |
dc.subject.nsi | VDP::Matematikk og naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420 | |
dc.subject.nsi | VDP::Mathematics and natural scienses: 400::Information and communication science: 420 | |
dc.relation.projectID | Norges forskningsråd: 263248 | |