{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T01:54:58Z","timestamp":1773366898346,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T00:00:00Z","timestamp":1623628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T00:00:00Z","timestamp":1623628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008530","name":"European Regional Development Fund","doi-asserted-by":"crossref","award":["MALEKA"],"award-info":[{"award-number":["MALEKA"]}],"id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Free and Hanseatic City of Hamburg","award":["MALEKA"],"award-info":[{"award-number":["MALEKA"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"published-print":{"date-parts":[[2021,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Intravascular ultrasound (IVUS) imaging is crucial for planning and performing percutaneous coronary interventions. Automatic segmentation of lumen and vessel wall in IVUS images can thus help streamlining the clinical workflow. State-of-the-art results in image segmentation are achieved with data-driven methods like convolutional neural networks (CNNs). These need large amounts of training data to perform sufficiently well but medical image datasets are often rather small. A possibility to overcome this problem is exploiting alternative network architectures like capsule networks.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We systematically investigated different capsule network architecture variants and optimized the performance on IVUS image segmentation. We then compared our capsule network with corresponding CNNs under varying amounts of training images and network parameters.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Contrary to previous works, our capsule network performs best when doubling the number of capsule types after each downsampling stage, analogous to typical increase rates of feature maps in CNNs. Maximum improvements compared to the baseline CNNs are 20.6% in terms of the Dice coefficient and 87.2% in terms of the average Hausdorff distance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Capsule networks are promising candidates when it comes to segmentation of small IVUS image datasets. We therefore assume that this also holds for ultrasound images in general. A reasonable next step would be the investigation of capsule networks for few- or even single-shot learning tasks.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-021-02417-x","type":"journal-article","created":{"date-parts":[[2021,6,14]],"date-time":"2021-06-14T13:02:36Z","timestamp":1623675756000},"page":"1243-1254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Capsule networks for segmentation of small intravascular ultrasound image datasets"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0610-0347","authenticated-orcid":false,"given":"Lennart","family":"Bargsten","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Silas","family":"Raschka","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexander","family":"Schlaefer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,6,14]]},"reference":[{"issue":"2","key":"2417_CR1","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compmedimag.2013.07.001","volume":"38","author":"S Balocco","year":"2014","unstructured":"Balocco S, Gatta C, Ciompi F, Wahle A, Radeva P, Carlier S, Unal G, Sanidas E, Mauri J, Carillo X, Kovarnik T, Wang CW, Chen HC, Exarchos TP, Fotiadis DI, Destrempes F, Cloutier G, Pujol O, Alberti M, Mendizabal-Ruiz EG, Rivera M, Aksoy T, Downe RW, Kakadiaris IA (2014) Standardized evaluation methodology and reference database for evaluating IVUS image segmentation. Comput Med Imaging Graph 38(2):70\u201390","journal-title":"Comput Med Imaging Graph"},{"issue":"9","key":"2417_CR2","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1007\/s11548-020-02203-1","volume":"15","author":"L Bargsten","year":"2020","unstructured":"Bargsten L, Schlaefer A (2020) SpeckleGAN: a generative adversarial network with an adaptive speckle layer to augment limited training data for ultrasound image processing. Int J Comput Assist Radiol Surg 15(9):1427\u20131436","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"2417_CR3","first-page":"664","volume":"2019","author":"S Bonheur","year":"2019","unstructured":"Bonheur S, \u0160tern D, Payer C, Pienn M, Olschewski H, Urschler M (2019) Matwo-capsnet: a multi-label semantic segmentation capsules network. Med Image Comput Comput Assist Interv 2019:664\u2013672","journal-title":"Med Image Comput Comput Assist Interv"},{"issue":"July 2017","key":"2417_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.compmedimag.2018.02.003","volume":"66","author":"F Chen","year":"2018","unstructured":"Chen F, Ma R, Liu J, Zhu M, Liao H (2018) Lumen and media-adventitia border detection in ivus images using texture enhanced deformable model. Comput Med Imaging Graph 66(July 2017):1\u201313","journal-title":"Comput Med Imaging Graph"},{"key":"2417_CR5","unstructured":"Dubuisson MP, Jain A (1994) A modified hausdorff distance for object matching. In: Proceedings of the 12th international conference on pattern recognition, pp 566\u2013568"},{"issue":"4","key":"2417_CR6","doi-asserted-by":"publisher","first-page":"134","DOI":"10.2478\/medu-2019-0018","volume":"2","author":"O Dzhioeva","year":"2019","unstructured":"Dzhioeva O (2019) Mobile ultrasound systems as a modern tool for the doctor. Med Univ 2(4):134\u2013138","journal-title":"Med Univ"},{"issue":"5","key":"2417_CR7","doi-asserted-by":"publisher","first-page":"1524","DOI":"10.1109\/TMI.2019.2952939","volume":"39","author":"Z Gao","year":"2020","unstructured":"Gao Z, Chung J, Abdelrazek M, Leung S, Hau WK, Xian Z, Zhang H, Li S (2020) Privileged modality distillation for vessel border detection in intracoronary imaging. IEEE Trans Med Imaging 39(5):1524\u20131534","journal-title":"IEEE Trans Med Imaging"},{"key":"2417_CR8","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"key":"2417_CR9","first-page":"44","volume":"2011","author":"GE Hinton","year":"2011","unstructured":"Hinton GE, Krizhevsky A, Wang SD (2011) Transforming auto-encoders. Artif Neural Netw Mach Learn 2011:44\u201351","journal-title":"Artif Neural Netw Mach Learn"},{"key":"2417_CR10","unstructured":"Hinton GE, Sabour S, Frosst N (2018) Matrix capsules with EM routing. In: International conference on learning representations"},{"key":"2417_CR11","doi-asserted-by":"crossref","unstructured":"Jayasundara V, Jayasekara S, Jayasekara H, Rajasegaran J, Seneviratne S, Rodrigo R (2019) Textcaps: handwritten character recognition with very small datasets. In: 2019 IEEE winter conference on applications of computer vision (WACV), pp 254\u2013262","DOI":"10.1109\/WACV.2019.00033"},{"key":"2417_CR12","doi-asserted-by":"crossref","unstructured":"Jim\u00e9nez-S\u00e1nchez A, Albarqouni S, Mateus D (2018) Capsule networks against medical imaging data challenges. In: Intravascular imaging and computer assisted stenting and large-scale annotation of biomedical data and expert label synthesis, pp 150\u2013160","DOI":"10.1007\/978-3-030-01364-6_17"},{"issue":"5","key":"2417_CR13","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1109\/TITB.2012.2189408","volume":"16","author":"A Katouzian","year":"2012","unstructured":"Katouzian A, Angelini ED, Carlier SG, Suri JS, Navab N, Laine AF (2012) A state-of-the-art review on segmentation algorithms in intravascular ultrasound (ivus) images. IEEE Trans Inf Technol Biomed 16(5):823\u2013834","journal-title":"IEEE Trans Inf Technol Biomed"},{"key":"2417_CR14","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.compbiomed.2018.10.024","volume":"104","author":"A Kermani","year":"2019","unstructured":"Kermani A, Ayatollahi A (2019) A new nonparametric statistical approach to detect lumen and media-adventitia borders in intravascular ultrasound frames. Comput Biol Med 104:10\u201328","journal-title":"Comput Biol Med"},{"key":"2417_CR15","doi-asserted-by":"crossref","unstructured":"Kim S, Jang Y, Jeon B, Hong Y, Shim H, Chang H (2018) Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images. In: Intravascular imaging and computer assisted stenting and large-scale annotation of biomedical data and expert label synthesis, pp 161\u2013168","DOI":"10.1007\/978-3-030-01364-6_18"},{"key":"2417_CR16","unstructured":"LaLonde R, Bagci U (2018) Capsules for object segmentation. ArXiv arXiv:1804.04241"},{"key":"2417_CR17","doi-asserted-by":"crossref","unstructured":"Li YC, Shen TY, Chen CC, Chang WT, Lee PY, Huang CC (2021) Automatic detection of atherosclerotic plaque and calcification from intravascular ultrasound images by using deep convolutional neural networks. IEEE Trans Ultrason Ferroelectr Freq Control","DOI":"10.1109\/TUFFC.2021.3052486"},{"key":"2417_CR18","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision (3DV), pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"2417_CR19","doi-asserted-by":"crossref","unstructured":"Nandamuri S, China D, Mitra P, Sheet D (2019) Sumnet: fully convolutional model for fast segmentation of anatomical structures in ultrasound volumes. In: 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019), pp 1729\u20131732","DOI":"10.1109\/ISBI.2019.8759210"},{"key":"2417_CR20","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention (MICCAI), pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"35","key":"2417_CR21","doi-asserted-by":"publisher","first-page":"3281","DOI":"10.1093\/eurheartj\/ehy285","volume":"39","author":"L R\u00e4ber","year":"2018","unstructured":"R\u00e4ber L, Mintz GS, Koskinas KC, Johnson TW, Holm NR, Onuma Y, Radu MD, Joner M, Yu B, Jia H, Meneveau N, de\u00a0la Torre\u00a0Hernandez JM, Escaned J, Hill J, Prati F, Colombo A, di Mario C, Regar E, Capodanno D, Wijns W, Byrne RA, Guagliumi G, Group ESD (2018) Clinical use of intracoronary imaging. Part 1: guidance and optimization of coronary interventions. An expert consensus document of the European Association of Percutaneous Cardiovascular Interventions. Eur Heart J 39(35):3281\u20133300","journal-title":"Eur Heart J"},{"key":"2417_CR22","unstructured":"Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Proceedings of the 31st international conference on neural information processing systems, pp 3859\u20133869"},{"key":"2417_CR23","first-page":"281","volume":"1994","author":"M Sonka","year":"1994","unstructured":"Sonka M, Zhang X, Siebes M, Dejong S, McKay CR, Collins SM (1994) Automated segmentation of coronary wall and plaque from intravascular ultrasound image sequences. Comput Cardiol 1994:281\u2013284","journal-title":"Comput Cardiol"},{"key":"2417_CR24","doi-asserted-by":"crossref","unstructured":"Sudre CH, Li W, Vercauteren T, Ourselin S, Jorge\u00a0Cardoso M (2017) Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp 240\u2013248","DOI":"10.1007\/978-3-319-67558-9_28"},{"key":"2417_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15:1\u201328","journal-title":"BMC Med Imaging"},{"issue":"3","key":"2417_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3386252","volume":"53","author":"Y Wang","year":"2020","unstructured":"Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: a survey on few-shot learning. ACM Comput Surv 53(3):1\u201334","journal-title":"ACM Comput Surv"},{"issue":"22","key":"2417_CR27","doi-asserted-by":"publisher","first-page":"4967","DOI":"10.3390\/app9224967","volume":"9","author":"M Xia","year":"2019","unstructured":"Xia M, Yan W, Huang Y, Guo Y, Zhou G, Wang Y (2019) Ivus image segmentation using superpixel-wise fuzzy clustering and level set evolution. Appl Sci 9(22):4967","journal-title":"Appl Sci"},{"key":"2417_CR28","doi-asserted-by":"crossref","unstructured":"Xia M, Yan W, Huang Y, Guo Y, Zhou G, Wang Y (2020) Extracting membrane borders in ivus images using a multi-scale feature aggregated u-net. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS, pp 1650\u20131653","DOI":"10.1109\/EMBC44109.2020.9175970"},{"key":"2417_CR29","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.ultras.2019.03.014","volume":"96","author":"J Yang","year":"2019","unstructured":"Yang J, Faraji M, Basu A (2019) Robust segmentation of arterial walls in intravascular ultrasound images using Dual Path U-Net. Ultrasonics 96:24\u201333","journal-title":"Ultrasonics"},{"key":"2417_CR30","doi-asserted-by":"crossref","unstructured":"Zhang X, Luo P, Hu X, Wang J, Zhou J (2018) Research on classification performance of small-scale dataset based on capsule network. In: Proceedings of the 4th international conference on robotics and artificial intelligence, pp 24\u201328","DOI":"10.1145\/3297097.3297105"},{"issue":"1","key":"2417_CR31","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1093\/ehjdh\/ztaa014","volume":"1","author":"PGP Ziemer","year":"2020","unstructured":"Ziemer PGP, Bulant CA, Orlando JI, Maso\u00a0Talou GD, \u00c1lvarez LAM, Guedes\u00a0Bezerra C, Lemos PA, Garc\u00eda-Garc\u00eda HM, Blanco PJ (2020) Automated lumen segmentation using multi-frame convolutional neural networks in intravascular ultrasound datasets. Eur Heart J Digit Health 1(1):75\u201382","journal-title":"Eur Heart J Digit Health"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02417-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-021-02417-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-021-02417-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,7,21]],"date-time":"2021-07-21T12:36:54Z","timestamp":1626871014000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-021-02417-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,14]]},"references-count":31,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2021,8]]}},"alternative-id":["2417"],"URL":"https:\/\/doi.org\/10.1007\/s11548-021-02417-x","relation":{},"ISSN":["1861-6410","1861-6429"],"issn-type":[{"value":"1861-6410","type":"print"},{"value":"1861-6429","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,14]]},"assertion":[{"value":"11 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 June 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}]}}