Analytical study of the encoder-decoder models for ultrasound image segmentation

Accurate diagnosis and treatment planning for medical conditions rely heavily on the results of medical image segmentation. Medical images are available in many modalities like CT scans, MRI, histopathological, and ultrasound images. Among all, the real-time analysis of the ultrasound is the most co...

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Vydáno v:	Service oriented computing and applications Ročník 18; číslo 1; s. 81 - 100
Hlavní autoři:	Srivastava, Somya, Vidyarthi, Ankit, Jain, Shikha
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.03.2024 Springer Nature B.V
Témata:	Algorithms Architecture Availability Computed tomography Computer Appl. in Administrative Data Processing Computer Science Computer Systems Organization and Communication Networks Datasets Deep learning Diagnostic systems e-Commerce/e-business Encoders-Decoders Image segmentation IT in Business Machine learning Management of Computing and Information Systems Medical imaging Nerves Neural networks Pipelining (computers) Real time Sensitivity Software Engineering/Programming and Operating Systems Special Issue Paper Ultrasonic imaging Deep neural network Encoder-decoder model Artificial intelligence Medical image segmentation
ISSN:	1863-2386, 1863-2394
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Abstract	Accurate diagnosis and treatment planning for medical conditions rely heavily on the results of medical image segmentation. Medical images are available in many modalities like CT scans, MRI, histopathological, and ultrasound images. Among all, the real-time analysis of the ultrasound is the most complex as the internal organ’s visualization requires experience from the radiologist. Diagnosing the medical conditions and unavailability of experienced radiologists during an emergency requires automated segmentation which heavily depends on computer-aided diagnostic systems. The new generation CAD systems are found to incorporate advanced deep learning algorithms to produce accurate segmentation results. While most of the segmentation models relate to the encoder-decoder model as the base architecture and thus evolve a variety of modifications in its pipeline architecture. This paper presents the analytical study of the various Encoder- Decoder based models like UNet, Residual UNet (Res-U-Net), Dense UNet (DenseUNet), Attention UNet, UNet + +, Double UNet, and U 2 Net (U-Squared-Net) on ultrasound image segmentation. Further, the paper presents the various trade-offs, application areas, open challenges, and performance analysis of these models on benchmark datasets, namely the HC18 Challenge dataset, CUM dataset, and B-mode Ultrasound Nerve Segmentation dataset. The performance analysis of these models is presented using the six state-of-the-art metrics like Dice coefficient, Jaccard index, sensitivity, specificity, Mean Absolute distance, and Housdorff Distance. Based on the above parameters U 2 -Net (U-Squared-Net) outperformed all other neural network models for all three datasets. In terms of all four criteria (Dice Coefficient: 0.92, 0.89, 0.9, Jaccard Index: 0.81, 0.79, 0.81, Sensitivity: 0.86, 0.84, 0.86, Specificity: 0.97, 0.95, 0.96), the U2-Net (U-Squared-Net) model performed the best. Over the HC18 Challenge dataset, the CUM dataset, and the B-Mode Ultrasound nerve segmentation dataset, U2-Net (U-Squared-Net) model achieved the best HD results (HC18: 3.8, CUM Dataset: 4, B-mode US Dataset: 3.6) and the lowest MAD values (HC18: 2.1, CUM Dataset: 3, B-mode US Dataset: 2.15). In addition, the analysis also highlights the architectural differences between these models, focusing on their type of connections, number of layers, and additional components. The outcome of this research provides valuable perception into the strengths and limitations of each encoder-decoder-based model, aiding researchers and practitioners in selecting the most appropriate model for Ultrasound image segmentation tasks.
AbstractList	Accurate diagnosis and treatment planning for medical conditions rely heavily on the results of medical image segmentation. Medical images are available in many modalities like CT scans, MRI, histopathological, and ultrasound images. Among all, the real-time analysis of the ultrasound is the most complex as the internal organ’s visualization requires experience from the radiologist. Diagnosing the medical conditions and unavailability of experienced radiologists during an emergency requires automated segmentation which heavily depends on computer-aided diagnostic systems. The new generation CAD systems are found to incorporate advanced deep learning algorithms to produce accurate segmentation results. While most of the segmentation models relate to the encoder-decoder model as the base architecture and thus evolve a variety of modifications in its pipeline architecture. This paper presents the analytical study of the various Encoder- Decoder based models like UNet, Residual UNet (Res-U-Net), Dense UNet (DenseUNet), Attention UNet, UNet + +, Double UNet, and U2Net (U-Squared-Net) on ultrasound image segmentation. Further, the paper presents the various trade-offs, application areas, open challenges, and performance analysis of these models on benchmark datasets, namely the HC18 Challenge dataset, CUM dataset, and B-mode Ultrasound Nerve Segmentation dataset. The performance analysis of these models is presented using the six state-of-the-art metrics like Dice coefficient, Jaccard index, sensitivity, specificity, Mean Absolute distance, and Housdorff Distance. Based on the above parameters U2-Net (U-Squared-Net) outperformed all other neural network models for all three datasets. In terms of all four criteria (Dice Coefficient: 0.92, 0.89, 0.9, Jaccard Index: 0.81, 0.79, 0.81, Sensitivity: 0.86, 0.84, 0.86, Specificity: 0.97, 0.95, 0.96), the U2-Net (U-Squared-Net) model performed the best. Over the HC18 Challenge dataset, the CUM dataset, and the B-Mode Ultrasound nerve segmentation dataset, U2-Net (U-Squared-Net) model achieved the best HD results (HC18: 3.8, CUM Dataset: 4, B-mode US Dataset: 3.6) and the lowest MAD values (HC18: 2.1, CUM Dataset: 3, B-mode US Dataset: 2.15). In addition, the analysis also highlights the architectural differences between these models, focusing on their type of connections, number of layers, and additional components. The outcome of this research provides valuable perception into the strengths and limitations of each encoder-decoder-based model, aiding researchers and practitioners in selecting the most appropriate model for Ultrasound image segmentation tasks. Accurate diagnosis and treatment planning for medical conditions rely heavily on the results of medical image segmentation. Medical images are available in many modalities like CT scans, MRI, histopathological, and ultrasound images. Among all, the real-time analysis of the ultrasound is the most complex as the internal organ’s visualization requires experience from the radiologist. Diagnosing the medical conditions and unavailability of experienced radiologists during an emergency requires automated segmentation which heavily depends on computer-aided diagnostic systems. The new generation CAD systems are found to incorporate advanced deep learning algorithms to produce accurate segmentation results. While most of the segmentation models relate to the encoder-decoder model as the base architecture and thus evolve a variety of modifications in its pipeline architecture. This paper presents the analytical study of the various Encoder- Decoder based models like UNet, Residual UNet (Res-U-Net), Dense UNet (DenseUNet), Attention UNet, UNet + +, Double UNet, and U 2 Net (U-Squared-Net) on ultrasound image segmentation. Further, the paper presents the various trade-offs, application areas, open challenges, and performance analysis of these models on benchmark datasets, namely the HC18 Challenge dataset, CUM dataset, and B-mode Ultrasound Nerve Segmentation dataset. The performance analysis of these models is presented using the six state-of-the-art metrics like Dice coefficient, Jaccard index, sensitivity, specificity, Mean Absolute distance, and Housdorff Distance. Based on the above parameters U 2 -Net (U-Squared-Net) outperformed all other neural network models for all three datasets. In terms of all four criteria (Dice Coefficient: 0.92, 0.89, 0.9, Jaccard Index: 0.81, 0.79, 0.81, Sensitivity: 0.86, 0.84, 0.86, Specificity: 0.97, 0.95, 0.96), the U2-Net (U-Squared-Net) model performed the best. Over the HC18 Challenge dataset, the CUM dataset, and the B-Mode Ultrasound nerve segmentation dataset, U2-Net (U-Squared-Net) model achieved the best HD results (HC18: 3.8, CUM Dataset: 4, B-mode US Dataset: 3.6) and the lowest MAD values (HC18: 2.1, CUM Dataset: 3, B-mode US Dataset: 2.15). In addition, the analysis also highlights the architectural differences between these models, focusing on their type of connections, number of layers, and additional components. The outcome of this research provides valuable perception into the strengths and limitations of each encoder-decoder-based model, aiding researchers and practitioners in selecting the most appropriate model for Ultrasound image segmentation tasks.
Author	Jain, Shikha Vidyarthi, Ankit Srivastava, Somya
Author_xml	– sequence: 1 givenname: Somya surname: Srivastava fullname: Srivastava, Somya organization: Department of Computer Science Engineering & Information Technology, Jaypee Institute of Information Technology – sequence: 2 givenname: Ankit orcidid: 0000-0002-8026-4246 surname: Vidyarthi fullname: Vidyarthi, Ankit email: dr.ankit.vidyarthi@gmail.com organization: Department of Computer Science Engineering & Information Technology, Jaypee Institute of Information Technology – sequence: 3 givenname: Shikha surname: Jain fullname: Jain, Shikha organization: Department of Computer Science Engineering & Information Technology, Jaypee Institute of Information Technology
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Cites_doi	10.1146/annurev.bioeng.2.1.315 10.1177/01617346221114137 10.1177/01617346211069882 10.11834/jig.190242 10.3390/life12111877 10.1007/s10278-020-00410-5 10.1109/TMI.2022.3226268 10.1002/mp.15700 10.1016/j.media.2017.07.005 10.1016/j.compbiomed.2023.106629 10.1109/TMI.2018.2845918 10.1016/j.bspc.2022.104425 10.1016/j.eswa.2023.119718 10.3390/diagnostics12123064 10.1016/j.compbiomed.2023.106792 10.1186/s12880-023-01011-8 10.1016/j.patcog.2020.107404 10.1038/s41598-023-29105-x 10.1016/j.cmpb.2023.107614 10.2174/1574362417666220513151926 10.1007/s11548-021-02430-0 10.1007/978-3-030-00889-5_1 10.1109/CBMS49503.2020.00111 10.12928/telkomnika.v18i3.14753 10.1016/j.bspc.2019.101626 10.1109/CIBCB48159.2020.9277667
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References	Joharah, Mohideen (CR23) 2022; 17 Qin, Zhang, Huang, Dehghan, Zaiane, Jagersand (CR13) 2020; 106 Xing, Yang, Tang, Zhang (CR31) 2020; 25 Chen, Li, Dai, Zhang, Yap (CR19) 2023; 42 AshkaniChenarlogh, GhelichOghli, Shabanzadeh, Sirjani, Akhavan, Shiri, Arabi, Sanei Taheri, Tarzamni (CR27) 2022; 44 CR12 CR11 He, Yang, Xie (CR20) 2023; 155 CR10 CR30 Zeng, Luo, Cheng, Lu (CR26) 2022; 49 Drozdzal, Vorontsov, Chartrand, Kadoury, Pal (CR5) 2016; 2016 Krithika Alias AnbuDevi, Suganthi (CR22) 2022; 12 Fakhry, Sayed, El-Baz (CR7) 2020; 55 Zheng, Qin, Cui, Wang, Zhao, Zhang, Zhao (CR14) 2023; 23 Zhang, Chen, McGough, Xie (CR6) 2021; 70 Litjens, Kooi, Bejnordi, Setio, Ciompi, Ghafoorian, Van Der Laak, Van Ginneken, Sánchez (CR2) 2017; 42 Zeng, Tsui, Wu, Zhou, Wu (CR29) 2021; 34 Zhu, Gao, Zhao, Zhu, Nan, Tian, Zhou (CR25) 2022; 44 Andreasen, Feragen, Christensen, Thybo, Svendsen, Zepf, Tolsgaard (CR15) 2023; 13 Balachandran, Qin, Jiang, Blouri, Forouzandeh, Dehghan, Punithakumar (CR18) 2023; 157 CR8 Li, Chen, Qi, Dou, Fu, Heng (CR9) 2018; 37 Mămuleanu, Urhuț, Săndulescu, Kamal, Pătrașcu, Ionescu, Șerbănescu, Streba (CR24) 2022; 12 Ronneberger, Fischer, Brox (CR3) 2015; 2015 Lyu, Xu, Jiang, Liu, Zhao, Zhu (CR21) 2023; 81 Bi, Cai, Sun, Jiang, Lu, Shu, Ni (CR16) 2023; 238 Iqbal, Sharif (CR17) 2023; 221 Pham, Xu, Prince (CR1) 2000; 2 Çiçek, Abdulkadir, Lienkamp, Brox, Ronneberger (CR4) 2016; 2016 Moccia, Fiorentino, Frontoni (CR28) 2021; 16 Y Zeng (373_CR29) 2021; 34 A Iqbal (373_CR17) 2023; 221 Y Xing (373_CR31) 2020; 25 F Joharah (373_CR23) 2022; 17 373_CR30 373_CR8 373_CR10 373_CR11 Ö Çiçek (373_CR4) 2016; 2016 LA Andreasen (373_CR15) 2023; 13 S Moccia (373_CR28) 2021; 16 M Krithika Alias AnbuDevi (373_CR22) 2022; 12 V AshkaniChenarlogh (373_CR27) 2022; 44 DL Pham (373_CR1) 2000; 2 T Zheng (373_CR14) 2023; 23 X Li (373_CR9) 2018; 37 Q He (373_CR20) 2023; 155 M Mămuleanu (373_CR24) 2022; 12 G Chen (373_CR19) 2023; 42 Z Zhang (373_CR6) 2021; 70 G Litjens (373_CR2) 2017; 42 373_CR12 A Fakhry (373_CR7) 2020; 55 W Zeng (373_CR26) 2022; 49 S Balachandran (373_CR18) 2023; 157 H Bi (373_CR16) 2023; 238 F Zhu (373_CR25) 2022; 44 O Ronneberger (373_CR3) 2015; 2015 Y Lyu (373_CR21) 2023; 81 M Drozdzal (373_CR5) 2016; 2016 X Qin (373_CR13) 2020; 106
References_xml	– volume: 2 start-page: 315 issue: 1 year: 2000 end-page: 337 ident: CR1 article-title: Current methods in medical image segmentation publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev.bioeng.2.1.315 – volume: 2016 start-page: 179 issue: 10008 year: 2016 end-page: 187 ident: CR5 article-title: The Importance of Skip Connections in Biomedical Image Segmentation publication-title: MICCAI – volume: 55 start-page: 101626 year: 2020 ident: CR7 article-title: Automated ultrasound image segmentation: a review publication-title: Biomed Signal Process Control – volume: 44 start-page: 191 issue: 5–6 year: 2022 end-page: 203 ident: CR25 article-title: A deep learning-based method to extract lumen and media-adventitia in intravascular ultrasound images publication-title: Ultrason Imag doi: 10.1177/01617346221114137 – volume: 44 start-page: 25 issue: 1 year: 2022 end-page: 38 ident: CR27 article-title: Fast and accurate U-net model for fetal ultrasound image segmentation publication-title: Ultrason Imag doi: 10.1177/01617346211069882 – volume: 25 start-page: 366 issue: 2 year: 2020 end-page: 377 ident: CR31 article-title: Ultrasound fetal head edge detection using fusion UNet++ publication-title: J Image Graph doi: 10.11834/jig.190242 – volume: 2015 start-page: 234 issue: 9351 year: 2015 end-page: 241 ident: CR3 article-title: UNet: convolutional networks for biomedical image segmentation publication-title: MICCAI – ident: CR12 – volume: 12 start-page: 1877 issue: 11 year: 2022 ident: CR24 article-title: Deep learning algorithms in the automatic segmentation of liver lesions in ultrasound investigations publication-title: Life doi: 10.3390/life12111877 – ident: CR30 – ident: CR10 – volume: 34 start-page: 134 issue: 1 year: 2021 end-page: 148 ident: CR29 article-title: Fetal ultrasound image segmentation for automatic head circumference biometry using deeply supervised attention-gated V-net publication-title: J Digit Imaging doi: 10.1007/s10278-020-00410-5 – ident: CR8 – volume: 42 start-page: 1289 issue: 5 year: 2023 end-page: 1300 ident: CR19 article-title: AAU-net: An adaptive attention U-net for breast lesions segmentation in ultrasound images publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2022.3226268 – volume: 2016 start-page: 424 issue: 9901 year: 2016 end-page: 432 ident: CR4 article-title: 3D UNet: Learning dense volumetric segmentation from sparse annotation publication-title: MICCAI – volume: 49 start-page: 5081 issue: 8 year: 2022 end-page: 5092 ident: CR26 article-title: Efficient fetal ultrasound image segmentation for automatic head circumference measurement using a lightweight deep convolutional neural network publication-title: Med Phys doi: 10.1002/mp.15700 – volume: 42 start-page: 60 year: 2017 end-page: 88 ident: CR2 article-title: A survey on deep learning in medical image analysis publication-title: Med Image Anal doi: 10.1016/j.media.2017.07.005 – volume: 155 start-page: 10669 year: 2023 ident: CR20 article-title: HCTNet: A hybrid CNN-transformer network for breast ultrasound image segmentation publication-title: Computer Biol Med doi: 10.1016/j.compbiomed.2023.106629 – volume: 37 start-page: 2663 issue: 12 year: 2018 end-page: 2674 ident: CR9 article-title: H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT Volumes publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2018.2845918 – volume: 81 start-page: 10445 year: 2023 ident: CR21 article-title: AMS-PAN: Breast ultrasound image segmentation model combining attention mechanism and multi-scale features publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2022.104425 – volume: 221 start-page: 119718 year: 2023 ident: CR17 article-title: PDF-UNet: A semi-supervised method for segmentation of breast tumor images using a U-shaped pyramid-dilated network publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.119718 – volume: 70 start-page: 101977 year: 2021 ident: CR6 article-title: A review on deep learning for ultrasound image segmentation publication-title: Med Image Anal – volume: 12 start-page: 3064 issue: 12 year: 2022 ident: CR22 article-title: Review of semantic segmentation of medical images using modified architectures of UNET publication-title: Diagnostics doi: 10.3390/diagnostics12123064 – volume: 157 start-page: 106792 year: 2023 ident: CR18 article-title: ACU2E-net: A novel predicts–refine attention network for segmentation of soft-tissue structures in ultrasound images publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2023.106792 – ident: CR11 – volume: 23 start-page: 56 issue: 1 year: 2023 ident: CR14 article-title: Segmentation of thyroid glands and nodules in ultrasound images using the improved U-net architecture publication-title: BMC Med Imag doi: 10.1186/s12880-023-01011-8 – volume: 106 start-page: 107404 year: 2020 ident: CR13 article-title: U2-Net: Going deeper with nested U-structure for salient object detection publication-title: Pattern Recogn doi: 10.1016/j.patcog.2020.107404 – volume: 13 start-page: 2221 issue: 1 year: 2023 ident: CR15 article-title: Multi-center deep learning for placenta segmentation in obstetric ultrasound with multi-observer and cross-country generalization publication-title: Sci Reports doi: 10.1038/s41598-023-29105-x – volume: 238 start-page: 107614 year: 2023 ident: CR16 article-title: BPAT-UNet: Boundary preserving assembled transformer UNet for ultrasound thyroid nodule segmentation publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2023.107614 – volume: 17 start-page: 57 issue: 3 year: 2022 end-page: 66 ident: CR23 article-title: Evaluation of fetal head circumference (HC) and biparietal diameter (BPD (biparietal diameter)) in ultrasound images using multi-task deep convolutional neural network publication-title: Curr Signal Transduct Ther doi: 10.2174/1574362417666220513151926 – volume: 16 start-page: 1711 issue: 10 year: 2021 end-page: 1718 ident: CR28 article-title: Mask-R 2 CNN: A distance-field regression version of mask-RCNN for fetal-head delineation in ultrasound images publication-title: Int J Comput Assist Radiol Surg doi: 10.1007/s11548-021-02430-0 – ident: 373_CR10 doi: 10.1007/978-3-030-00889-5_1 – volume: 23 start-page: 56 issue: 1 year: 2023 ident: 373_CR14 publication-title: BMC Med Imag doi: 10.1186/s12880-023-01011-8 – volume: 17 start-page: 57 issue: 3 year: 2022 ident: 373_CR23 publication-title: Curr Signal Transduct Ther doi: 10.2174/1574362417666220513151926 – volume: 81 start-page: 10445 year: 2023 ident: 373_CR21 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2022.104425 – volume: 42 start-page: 60 year: 2017 ident: 373_CR2 publication-title: Med Image Anal doi: 10.1016/j.media.2017.07.005 – volume: 2016 start-page: 424 issue: 9901 year: 2016 ident: 373_CR4 publication-title: MICCAI – volume: 44 start-page: 25 issue: 1 year: 2022 ident: 373_CR27 publication-title: Ultrason Imag doi: 10.1177/01617346211069882 – volume: 49 start-page: 5081 issue: 8 year: 2022 ident: 373_CR26 publication-title: Med Phys doi: 10.1002/mp.15700 – volume: 70 start-page: 101977 year: 2021 ident: 373_CR6 publication-title: Med Image Anal – volume: 12 start-page: 3064 issue: 12 year: 2022 ident: 373_CR22 publication-title: Diagnostics doi: 10.3390/diagnostics12123064 – volume: 13 start-page: 2221 issue: 1 year: 2023 ident: 373_CR15 publication-title: Sci Reports doi: 10.1038/s41598-023-29105-x – volume: 155 start-page: 10669 year: 2023 ident: 373_CR20 publication-title: Computer Biol Med doi: 10.1016/j.compbiomed.2023.106629 – volume: 16 start-page: 1711 issue: 10 year: 2021 ident: 373_CR28 publication-title: Int J Comput Assist Radiol Surg doi: 10.1007/s11548-021-02430-0 – ident: 373_CR12 doi: 10.1109/CBMS49503.2020.00111 – volume: 37 start-page: 2663 issue: 12 year: 2018 ident: 373_CR9 publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2018.2845918 – ident: 373_CR11 doi: 10.12928/telkomnika.v18i3.14753 – volume: 238 start-page: 107614 year: 2023 ident: 373_CR16 publication-title: Comput Methods Programs Biomed doi: 10.1016/j.cmpb.2023.107614 – volume: 42 start-page: 1289 issue: 5 year: 2023 ident: 373_CR19 publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2022.3226268 – volume: 12 start-page: 1877 issue: 11 year: 2022 ident: 373_CR24 publication-title: Life doi: 10.3390/life12111877 – volume: 44 start-page: 191 issue: 5–6 year: 2022 ident: 373_CR25 publication-title: Ultrason Imag doi: 10.1177/01617346221114137 – volume: 2015 start-page: 234 issue: 9351 year: 2015 ident: 373_CR3 publication-title: MICCAI – volume: 25 start-page: 366 issue: 2 year: 2020 ident: 373_CR31 publication-title: J Image Graph doi: 10.11834/jig.190242 – volume: 34 start-page: 134 issue: 1 year: 2021 ident: 373_CR29 publication-title: J Digit Imaging doi: 10.1007/s10278-020-00410-5 – volume: 2 start-page: 315 issue: 1 year: 2000 ident: 373_CR1 publication-title: Annu Rev Biomed Eng doi: 10.1146/annurev.bioeng.2.1.315 – volume: 55 start-page: 101626 year: 2020 ident: 373_CR7 publication-title: Biomed Signal Process Control doi: 10.1016/j.bspc.2019.101626 – ident: 373_CR8 – ident: 373_CR30 doi: 10.1109/CIBCB48159.2020.9277667 – volume: 221 start-page: 119718 year: 2023 ident: 373_CR17 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2023.119718 – volume: 157 start-page: 106792 year: 2023 ident: 373_CR18 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2023.106792 – volume: 106 start-page: 107404 year: 2020 ident: 373_CR13 publication-title: Pattern Recogn doi: 10.1016/j.patcog.2020.107404 – volume: 2016 start-page: 179 issue: 10008 year: 2016 ident: 373_CR5 publication-title: MICCAI
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SubjectTerms	Algorithms Architecture Availability Computed tomography Computer Appl. in Administrative Data Processing Computer Science Computer Systems Organization and Communication Networks Datasets Deep learning Diagnostic systems e-Commerce/e-business Encoders-Decoders Image segmentation IT in Business Machine learning Management of Computing and Information Systems Medical imaging Nerves Neural networks Pipelining (computers) Real time Sensitivity Software Engineering/Programming and Operating Systems Special Issue Paper Ultrasonic imaging
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Title	Analytical study of the encoder-decoder models for ultrasound image segmentation
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