3D shallow deep neural network for fast and precise segmentation of left atrium
Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions. Hence, an automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of heart. However, due to the sma...
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| Published in: | Multimedia systems Vol. 29; no. 3; pp. 1739 - 1749 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.06.2023
Springer Nature B.V |
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| ISSN: | 0942-4962, 1432-1882 |
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| Abstract | Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions. Hence, an automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of heart. However, due to the small size of left atrium with respect to the whole MRI volume, accurate segmentation of left atrium is challenging. Most of the existing deep learning approaches are based on cropping or cascading networks. In this work, we present a novel deep learning architecture for the segmentation of left atrium from MRI volume which incorporates the residual learning based encoder-decoder network. We introduce a loss function and parameter adjustments to deal with the issue of class imbalance and unavailability of large medical imaging dataset. To facilitate the high quality segmentation, we present a three-dimensional multi-scale residual learning based architecture that maintains coarse and fine level features throughout the network. Experimental results have shown a considerable improvement in segmentation performance by surpassing the current benchmarks (especially the winner of Left Atrial Segmentation Challenge-2018) with fewer parameters compared to the state-of-the-art approaches, thus potentially supporting cardiac diagnosis and surgery without adding any extensive pre-processing of input volumes or any post-processing on the base network’s output. |
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| AbstractList | Knowledge of the underlying anatomy of the left atrium can promote improved diagnostic protocols and clinical interventions. Hence, an automatic segmentation of the left atrium on magnetic resonance imaging (MRI) can support diagnosis, treatment and surgery planning of heart. However, due to the small size of left atrium with respect to the whole MRI volume, accurate segmentation of left atrium is challenging. Most of the existing deep learning approaches are based on cropping or cascading networks. In this work, we present a novel deep learning architecture for the segmentation of left atrium from MRI volume which incorporates the residual learning based encoder-decoder network. We introduce a loss function and parameter adjustments to deal with the issue of class imbalance and unavailability of large medical imaging dataset. To facilitate the high quality segmentation, we present a three-dimensional multi-scale residual learning based architecture that maintains coarse and fine level features throughout the network. Experimental results have shown a considerable improvement in segmentation performance by surpassing the current benchmarks (especially the winner of Left Atrial Segmentation Challenge-2018) with fewer parameters compared to the state-of-the-art approaches, thus potentially supporting cardiac diagnosis and surgery without adding any extensive pre-processing of input volumes or any post-processing on the base network’s output. |
| Author | Beheshti, Amin Razzak, Imran Shapiai, Mohammad Ibrahim Kausar, Asma |
| Author_xml | – sequence: 1 givenname: Asma orcidid: 0000-0002-7853-1819 surname: Kausar fullname: Kausar, Asma email: aftabasma@graduate.utm.my organization: MJIIT, Universiti Teknologi Malaysia – sequence: 2 givenname: Imran surname: Razzak fullname: Razzak, Imran organization: Deakin University – sequence: 3 givenname: Mohammad Ibrahim surname: Shapiai fullname: Shapiai, Mohammad Ibrahim organization: MJIIT, Universiti Teknologi Malaysia – sequence: 4 givenname: Amin surname: Beheshti fullname: Beheshti, Amin organization: Macquire University |
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| Cites_doi | 10.1016/j.media.2016.01.005 10.1007/s00034-019-01246-3 10.1016/j.compbiomed.2019.103444 10.1109/TPAMI.2016.2577031 10.1109/TPAMI.2016.2572683 10.1016/j.acra.2012.03.022 10.1007/BF03015764 10.1109/TMI.2018.2845918 10.1186/1532-429X-12-65 10.1038/nature14539 10.1093/ehjci/jev014 10.1002/mrm.26631 10.1111/jce.12199 10.1109/TIP.2005.852470 10.1109/TMI.2015.2398818 10.1016/j.media.2016.10.004 10.1109/TPAMI.2012.231 10.1161/CIRCIMAGING.112.980037 10.15420/cfr.2016.2.2.115 10.1109/TPAMI.2017.2699184 10.1109/TMI.2018.2806309 10.1007/978-3-030-12029-0 10.1007/978-3-319-24574-4_28 10.1007/978-3-319-59050-9_28 10.1186/s12968-018-0471-x 10.1007/978-3-319-10584-0_20 10.1007/978-3-319-75238-9_25 10.1007/978-3-319-75541-0_13 10.1007/978-3-319-10584-0_23 10.1007/978-3-319-75238-9_38 10.1007/978-3-030-12029-0_23 10.1007/978-3-658-25326-4_7 10.1007/978-3-319-46493-0_38 10.1145/3065386 |
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| Keywords | Deep learning CNN Left atrium Segmentation Cardiac segmentation Shallow network |
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| SubjectTerms | Accuracy Algorithms Artificial neural networks Atria Cardiac arrhythmia Clinical medicine Computer Communication Networks Computer Graphics Computer Science Cryptology Data Storage Representation Datasets Deep learning Diagnosis Encoders-Decoders Heart Image segmentation Machine learning Magnetic resonance imaging Medical imaging Multimedia Information Systems Neural networks Operating Systems Parameters Role of Deep Learning Models & Analytics in Industrial Multimedia Environment Semantics Special Issue Paper Surgery |
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