Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling最新文献_第9页

Dual-camera laparoscopic imaging with super-resolution reconstruction for intraoperative hyperspectral image guidance 双摄像头腹腔镜成像与超分辨率重建，用于术中高光谱图像引导

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling Pub Date : 2024-03-29 DOI: 10.1117/12.3006573

Ling Ma, Kelden Pruitt, Baowei Fei

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Image guidance system for breast conserving surgery with integrated stereo camera monitoring and deformable correction 集成立体摄像机监控和可变形校正功能的保乳手术图像引导系统

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling Pub Date : 2024-03-29 DOI: 10.1117/12.3007858

M. Ringel, W. Richey, Jon S. Heiselman, Alexander W. Stabile, I. Meszoely, Michael I Miga

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Intraoperative tracked ultrasound imaging for resolving deformations during spine surgery 用于解决脊柱手术中变形问题的术中跟踪超声成像技术

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling Pub Date : 2024-03-29 DOI: 10.1117/12.3006919

Jinchi Wei, D. China, Kai Ding, Neil Crawford, Norbert Johnson, Nicholas Theodore, A. Uneri

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Enhancing MR-guided laser interstitial thermal therapy planning using U-Net: a data-driven approach for predicting MR thermometry images 利用 U-Net 加强磁共振引导下的激光间质热疗规划：预测磁共振测温图像的数据驱动方法

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling Pub Date : 2024-03-29 DOI: 10.1117/12.3006041

Saba Sadatamin, Sara Ketabi, Elise Donszelmann-Lund, Saba Abtahi, Yuri Chaban, Steven Robbins, Richard Tyc, Farzad Khalvati, A. Waspe, L. Kahrs, James M Drake

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Automatic auditory nerve fiber localization using geodesic paths. 利用大地路径自动定位听觉神经纤维

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling Pub Date : 2024-03-29 DOI: 10.1117/12.3008585

Ziteng Liu, Erin Bratu, Jack H. Noble

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Design of coronary artery phantom using polyvinyl alcohol cryogel for optical coherence tomography imaging in Kawasaki Disease 利用聚乙烯醇冷冻凝胶设计冠状动脉模型，用于川崎病的光学相干断层扫描成像

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling Pub Date : 2024-03-29 DOI: 10.1117/12.3006698

Matilde Pazzaglia, A. Abdolmanafi, Gerardo Tibamoso Pedraza, N. Dahdah, Luc Duong

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Benchmarking image transformers for prostate cancer detection from ultrasound data 从超声波数据中检测前列腺癌的图像变换器基准测试

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling Pub Date : 2024-03-27 DOI: 10.1117/12.3006049

Mohamed Harmanani, P. Wilson, F. Fooladgar, A. Jamzad, Mahdi Gilany, Minh Nguyen Nhat To, B. Wodlinger, P. Abolmaesumi, P. Mousavi

{"title":"Benchmarking image transformers for prostate cancer detection from ultrasound data","authors":"Mohamed Harmanani, P. Wilson, F. Fooladgar, A. Jamzad, Mahdi Gilany, Minh Nguyen Nhat To, B. Wodlinger, P. Abolmaesumi, P. Mousavi","doi":"10.1117/12.3006049","DOIUrl":"https://doi.org/10.1117/12.3006049","url":null,"abstract":"PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) to detect cancer in small regions of interest (ROI) along a needle trace region. However, this approach suffers from weak labelling, since the ground-truth histopathology labels do not describe the properties of individual ROIs. Recently, multi-scale approaches have sought to mitigate this issue by combining the context awareness of transformers with a CNN feature extractor to detect cancer from multiple ROIs using multiple-instance learning (MIL). In this work, we present a detailed study of several image transformer architectures for both ROI-scale and multi-scale classification, and a comparison of the performance of CNNs and transformers for ultrasound-based prostate cancer classification. We also design a novel multi-objective learning strategy that combines both ROI and core predictions to further mitigate label noise. METHODS: We evaluate 3 image transformers on ROI-scale cancer classification, then use the strongest model to tune a multi-scale classifier with MIL. We train our MIL models using our novel multi-objective learning strategy and compare our results to existing baselines. RESULTS: We find that for both ROI-scale and multi-scale PCa detection, image transformer backbones lag behind their CNN counterparts. This deficit in performance is even more noticeable for larger models. When using multi-objective learning, we can improve performance of MIL, with a 77.9% AUROC, a sensitivity of 75.9%, and a specificity of 66.3%. CONCLUSION: Convolutional networks are better suited for modelling sparse datasets of prostate ultrasounds, producing more robust features than transformers in PCa detection. Multi-scale methods remain the best architecture for this task, with multi-objective learning presenting an effective way to improve performance.","PeriodicalId":517504,"journal":{"name":"Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling","volume":"48 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140376862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring optical flow inclusion into nnU-Net framework for surgical instrument segmentation 探索将光学流纳入 nnU-Net 框架以进行手术器械分割

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling Pub Date : 2024-03-15 DOI: 10.1117/12.3006855

Marcos Fernández-Rodríguez, Bruno Silva, Sandro Queirós, Helena R. Torres, Bruno Oliveira, P. Morais, L. R. Buschle, Jorge Correia-Pinto, Estevão Lima, João L. Vilaça

{"title":"Exploring optical flow inclusion into nnU-Net framework for surgical instrument segmentation","authors":"Marcos Fernández-Rodríguez, Bruno Silva, Sandro Queirós, Helena R. Torres, Bruno Oliveira, P. Morais, L. R. Buschle, Jorge Correia-Pinto, Estevão Lima, João L. Vilaça","doi":"10.1117/12.3006855","DOIUrl":"https://doi.org/10.1117/12.3006855","url":null,"abstract":"Surgical instrument segmentation in laparoscopy is essential for computer-assisted surgical systems. Despite the Deep Learning progress in recent years, the dynamic setting of laparoscopic surgery still presents challenges for precise segmentation. The nnU-Net framework excelled in semantic segmentation analyzing single frames without temporal information. The framework's ease of use, including its ability to be automatically configured, and its low expertise requirements, have made it a popular base framework for comparisons. Optical flow (OF) is a tool commonly used in video tasks to estimate motion and represent it in a single frame, containing temporal information. This work seeks to employ OF maps as an additional input to the nnU-Net architecture to improve its performance in the surgical instrument segmentation task, taking advantage of the fact that instruments are the main moving objects in the surgical field. With this new input, the temporal component would be indirectly added without modifying the architecture. Using CholecSeg8k dataset, three different representations of movement were estimated and used as new inputs, comparing them with a baseline model. Results showed that the use of OF maps improves the detection of classes with high movement, even when these are scarce in the dataset. To further improve performance, future work may focus on implementing other OF-preserving augmentations.","PeriodicalId":517504,"journal":{"name":"Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling","volume":"52 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140283845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0