Juan Prieto, Chiraz Benabdelkader, Teeranan Pokaprakarn, Hina Shah, Yuri Sebastião, Qing Dan, Nariman Almnini, Arieska Nicole Diaz, Srihari Chari, Harmony Chi, Elizabeth Stringer, Jeffrey Stringer
{"title":"SimNorth: A novel contrastive learning approach for clustering prenatal ultrasound images.","authors":"Juan Prieto, Chiraz Benabdelkader, Teeranan Pokaprakarn, Hina Shah, Yuri Sebastião, Qing Dan, Nariman Almnini, Arieska Nicole Diaz, Srihari Chari, Harmony Chi, Elizabeth Stringer, Jeffrey Stringer","doi":"10.1007/978-3-031-44521-7_10","DOIUrl":"10.1007/978-3-031-44521-7_10","url":null,"abstract":"<p><p>This paper describes SimNorth, an unsupervised learning approach for classifying non-standard fetal ultrasound images. SimNorth utilizes a deep feature learning model with a novel contrastive loss function to project images with similar characteristics closer together in an embedding space while pushing apart those with different image features. We then use non-linear dimensionality reduction via t-SNE and apply standard clustering algorithms such as k-means and dbscan in 2D embedding space to identify clusters containing similar fetal structures. We compare SimNorth to other unsupervised learning techniques (such as Autoencoders, MoCo, and SimCLR) and demonstrate its superior performance based on cluster purity measures.</p>","PeriodicalId":520253,"journal":{"name":"Simplifying medical ultrasound : 4th International Workshop, ASMUS 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ASMUS (Workshop) (4th : 2023 : Vancouver, B.C. ; Online)","volume":"14337 ","pages":"100-108"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12127854/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Stojanovski, Uxio Hermida, Pablo Lamata, Arian Beqiri, Alberto Gomez
{"title":"Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation.","authors":"David Stojanovski, Uxio Hermida, Pablo Lamata, Arian Beqiri, Alberto Gomez","doi":"10.1007/978-3-031-44521-7_4","DOIUrl":"https://doi.org/10.1007/978-3-031-44521-7_4","url":null,"abstract":"<p><p>We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps. We show that these synthetic images can serve as a viable substitute for real data in the training of deep-learning models for ultrasound image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic 2D echocardiograms and trained a neural network for segmenting the left ventricle and left atrium. The performance of the network trained on exclusively synthetic images was evaluated on an unseen dataset of real images and yielded mean Dice scores of 88.6 <i>±</i>4.91, 91.9 <i>±</i>4.22, 85.2 <i>±</i>4.83 % for left ventricular endocardium, epicardium and left atrial segmentation respectively. This represents a relative increase of 9.2, 3.3 and 13.9 % in Dice scores compared to the previous state-of-the-art. The proposed pipeline has potential for application to a wide range of other tasks across various medical imaging modalities.</p>","PeriodicalId":520253,"journal":{"name":"Simplifying medical ultrasound : 4th International Workshop, ASMUS 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings. ASMUS (Workshop) (4th : 2023 : Vancouver, B.C. ; Online)","volume":"14337 ","pages":"34-43"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142485600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}