| 1. Identity statement | ||||
| Reference Type | Conference Paper (Conference Proceedings) | |||
| Site | sibgrapi.sid.inpe.br | |||
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH | |||
| Identifier | 8JMKD3MGPEW34M/3U39S3S | |||
| Repository | sid.inpe.br/sibgrapi/2019/09.13.22.34 | |||
| Last Update | 2019:09.13.22.34.58 (UTC) administrator | |||
| Metadata Repository | sid.inpe.br/sibgrapi/2019/09.13.22.34.58 | |||
| Metadata Last Update | 2022:06.14.00.09.41 (UTC) administrator | |||
| DOI | 10.1109/SIBGRAPI.2019.00017 | |||
| Citation Key | Gutierrez-CastillaToFaKoScMaMo:2019:ExMuIn | |||
| Title | Long-Range Decoder Skip Connections: Exploiting Multi-Context Information for Cardiac Image Segmentation  | |||
| Format | On-line | |||
| Year | 2019 | |||
| Access Date | 2024, May 01 | |||
| Number of Files | 1 | |||
| Size | 808 KiB | |||
| 2. Context | ||||
| Author | 1 Gutierrez-Castilla, Nicolás 2 Torres, Ricardo da Silva 3 Falcão, Alexandre Xavier 4 Kozerke, Sebastian 5 Schwitter, Jürg 6 Masci, Pier-Giorgio 7 Montoya-Zegarra, Javier A. | |||
| Affiliation | 1 Department of Computer Science, San Pablo Catholic University, Arequipa, Perú 2 Institute of Computing, University of Campinas, Campinas, SP, Brazil 3 Institute of Computing, University of Campinas, Campinas, SP, Brazil 4 Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland 5 Center for Cardiac Magnetic Resonance, Lausanne University Hospital, Lausanne, Switzerland 6 Rayne Institute School of Bioengineering and Imaging Sciences, King’s College London, London, United Kingdom 7 Institute for Biomedical Engineering, ETH Zurich, Zurich, Switzerland | |||
| Editor | Oliveira, Luciano Rebouças de Sarder, Pinaki Lage, Marcos Sadlo, Filip | |||
| e-Mail Address | nnicorast@gmail.com | |||
| Conference Name | Conference on Graphics, Patterns and Images, 32 (SIBGRAPI) | |||
| Conference Location | Rio de Janeiro, RJ, Brazil | |||
| Date | 28-31 Oct. 2019 | |||
| Publisher | IEEE Computer Society | |||
| Publisher City | Los Alamitos | |||
| Book Title | Proceedings | |||
| Tertiary Type | Full Paper | |||
| History (UTC) | 2019-09-13 22:34:58 :: nnicorast@gmail.com -> administrator :: 2022-06-14 00:09:41 :: administrator -> nnicorast@gmail.com :: 2019 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Version Type | finaldraft | |||
| Keywords | semantic image segmentation deep learning cardiac image analysis biomedical imaging | |||
| Abstract | The heart is one of the most important organs in our body and many critical diseases are associated with its malfunctioning. To assess the risk for heart diseases, Magnetic Resonance Imaging (MRI) has become the golden standard imaging technique, as it provides to the clinicians stacks of images for analyzing the heart structures, such as the ventricles, and thus to make a diagnosis of the patients health. The problem is that examination of these stacks, often based on the delineation of heart structures, is tedious and error prone due to inter- and intra-variability among manual delineations. For this reason,the investigation of fully automated methods to support heart segmentation is paramount. Most of the successful methods proposed to solve this problem are based on deep-learning solutions.Especially, encoder-decoder architectures, such as the U-Net [1],have demonstrated to be very effective architectures for medical image segmentation. In this paper, we propose to use long-range skip connections on the decoder-part to incorporate multi-context information onto the predicted segmentation masks and also to improve the generalization of the models. In addition, our method obtains smoother segmentations through the combination of feature maps from different stages onto the final prediction layer. We evaluate our approach in the ACDC [2] and LVSC [3] heart segmentation challenges. Experiments performed on both datasets demonstrate that our approach leads to an improvement on both the total Dice score and the Ejection Fraction Correlation, when combined with state-of-the-art encoder-decoder architectures. | |||
| Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2019 > Long-Range Decoder Skip... | |||
| Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Long-Range Decoder Skip... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/3U39S3S | |||
| zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/3U39S3S | |||
| Language | en | |||
| Target File | 101.pdf | |||
| User Group | nnicorast@gmail.com | |||
| Visibility | shown | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 | |||
| Next Higher Units | 8JMKD3MGPEW34M/3UA4FNL 8JMKD3MGPEW34M/3UA4FPS 8JMKD3MGPEW34M/4742MCS | |||
| Citing Item List | sid.inpe.br/sibgrapi/2019/10.25.18.30.33 1 | |||
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 | |||
| 6. Notes | ||||
| Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume | |||
| 7. Description control | ||||
| e-Mail (login) | nnicorast@gmail.com | |||
| update | ||||