Simplifying medical ultrasound : 5th international workshop, ASMUS 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, proceedings. ASMUS (Workshop) (5th : 2024 : Marrakech, Morocco)最新文献

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Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images. 基于交互式分割模型的胎盘三维超声图像分割。

Simplifying medical ultrasound : 5th international workshop, ASMUS 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, proceedings. ASMUS (Workshop) (5th : 2024 : Marrakech, Morocco) Pub Date : 2025-01-01 Epub Date: 2024-10-05 DOI: 10.1007/978-3-031-73647-6_13

Hao Li, Baris Oguz, Gabriel Arenas, Xing Yao, Jiacheng Wang, Alison Pouch, Brett Byram, Nadav Schwartz, Ipek Oguz

{"title":"Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images.","authors":"Hao Li, Baris Oguz, Gabriel Arenas, Xing Yao, Jiacheng Wang, Alison Pouch, Brett Byram, Nadav Schwartz, Ipek Oguz","doi":"10.1007/978-3-031-73647-6_13","DOIUrl":"10.1007/978-3-031-73647-6_13","url":null,"abstract":"Placenta volume measurement from 3D ultrasound images is critical for predicting pregnancy outcomes, and manual annotation is the gold standard. However, such manual annotation is expensive and time consuming. Automated segmentation algorithms can often successfully segment the placenta, but these methods may not consistently produce robust segmentations suitable for practical use. Recently, inspired by the Segment Anything Model (SAM), deep learning-based interactive segmentation models have been widely applied in the medical imaging domain. These models produce a segmentation from visual prompts provided to indicate the target region, which may offer a feasible solution for practical use. However, none of these models are specifically designed for interactively segmenting 3D ultrasound images, which remain challenging due to the inherent noise of this modality. In this paper, we evaluate publicly available state-of-the-art 3D interactive segmentation models in contrast to a human-in-the-loop approach for the placenta segmentation task. The Dice score, normalized surface Dice, averaged symmetric surface distance, and 95-percent Hausdorff distance are used as evaluation metrics. We consider a Dice score of 0.95 a successful segmentation. Our results indicate that the human-in-the-loop segmentation model reaches this standard. Moreover, we assess the efficiency of the human-in-the-loop model as a function of the amount of prompts. Our results demonstrate that the human-in-the-loop model is both effective and efficient for interactive placenta segmentation. The code is available at https://github.com/MedICL-VU/PRISM-placenta.","PeriodicalId":520353,"journal":{"name":"Simplifying medical ultrasound : 5th international workshop, ASMUS 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, proceedings. ASMUS (Workshop) (5th : 2024 : Marrakech, Morocco)","volume":"15186 ","pages":"132-142"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12128789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144218473","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}

引用次数: 0

Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound. 学习术前磁共振和术中超声的二维关键点匹配。

Hassan Rasheed, Reuben Dorent, Maximilian Fehrentz, Daniil Morozov, Tina Kapur, William M Wells, Alexandra Golby, Sarah Frisken, Julia A Schnabel, Nazim Haouchine

{"title":"Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound.","authors":"Hassan Rasheed, Reuben Dorent, Maximilian Fehrentz, Daniil Morozov, Tina Kapur, William M Wells, Alexandra Golby, Sarah Frisken, Julia A Schnabel, Nazim Haouchine","doi":"10.1007/978-3-031-73647-6_8","DOIUrl":"10.1007/978-3-031-73647-6_8","url":null,"abstract":"We propose in this paper a texture-invariant 2D keypoints descriptor specifically designed for matching preoperative Magnetic Resonance (MR) images with intraoperative Ultrasound (US) images. We introduce a matching-by-synthesis strategy, where intraoperative US images are synthesized from MR images accounting for multiple MR modalities and intraoperative US variability. We build our training set by enforcing keypoints localization over all images then train a patient-specific descriptor network that learns texture-invariant discriminant features in a supervised contrastive manner, leading to robust keypoints descriptors. Our experiments on real cases with ground truth show the effectiveness of the proposed approach, outperforming the state-of-the-art methods and achieving 80.35% matching precision on average.","PeriodicalId":520353,"journal":{"name":"Simplifying medical ultrasound : 5th international workshop, ASMUS 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 6, 2024, proceedings. ASMUS (Workshop) (5th : 2024 : Marrakech, Morocco)","volume":"15186 ","pages":"78-87"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11682695/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142908173","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}

引用次数: 0