Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ...最新文献

筛选

英文中文

Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors. 利用三维头部先验，从可见面部地标自动检测神经颅骨地标

Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ... Pub Date : 2023-10-01 Epub Date: 2023-10-09 DOI: 10.1007/978-3-031-45249-9_2

Oded Schlesinger, Raj Kundu, Stefan Goetz, Guillermo Sapiro, Angel V Peterchev, J Matias Di Martino

{"title":"Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors.","authors":"Oded Schlesinger, Raj Kundu, Stefan Goetz, Guillermo Sapiro, Angel V Peterchev, J Matias Di Martino","doi":"10.1007/978-3-031-45249-9_2","DOIUrl":"10.1007/978-3-031-45249-9_2","url":null,"abstract":"The localization and tracking of neurocranial landmarks is essential in modern medical procedures, e.g., transcranial magnetic stimulation (TMS). However, state-of-the-art treatments still rely on the manual identification of head targets and require setting retroreflective markers for tracking. This limits the applicability and scalability of TMS approaches, making them time-consuming, dependent on expensive hardware, and prone to errors when retroreflective markers drift from their initial position. To overcome these limitations, we propose a scalable method capable of inferring the position of points of interest on the scalp, e.g., the International 10-20 System's neurocranial landmarks. In contrast with existing approaches, our method does not require human intervention or markers; head landmarks are estimated leveraging visible facial landmarks, optional head size measurements, and statistical head model priors. We validate the proposed approach on ground truth data from 1,150 subjects, for which facial 3D and head information is available; our technique achieves a localization RMSE of 2.56 mm on average, which is of the same order as reported by high-end techniques in TMS. Our implementation is available at https://github.com/odedsc/ANLD.","PeriodicalId":510889,"journal":{"name":"Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ...","volume":"14242 ","pages":"12-20"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10752036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139059405","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

Automated Orientation and Registration of Cone-Beam Computed Tomography Scans. 锥形束计算机断层扫描的自动定位和配准。

Luc Anchling, Nathan Hutin, Yanjie Huang, Selene Barone, Sophie Roberts, Felicia Miranda, Marcela Gurgel, Najla Al Turkestani, Sara Tinawi, Jonas Bianchi, Marilia Yatabe, Antonio Ruellas, Juan Carlos Prieto, Lucia Cevidanes

{"title":"Automated Orientation and Registration of Cone-Beam Computed Tomography Scans.","authors":"Luc Anchling, Nathan Hutin, Yanjie Huang, Selene Barone, Sophie Roberts, Felicia Miranda, Marcela Gurgel, Najla Al Turkestani, Sara Tinawi, Jonas Bianchi, Marilia Yatabe, Antonio Ruellas, Juan Carlos Prieto, Lucia Cevidanes","doi":"10.1007/978-3-031-45249-9_5","DOIUrl":"10.1007/978-3-031-45249-9_5","url":null,"abstract":"Automated clinical decision support systems rely on accurate analysis of three-dimensional (3D) medical and dental images to assist clinicians in diagnosis, treatment planning, intervention, and assessment of growth and treatment effects. However, analyzing longitudinal 3D images requires standardized orientation and registration, which can be laborious and error-prone tasks dependent on structures of reference for registration. This paper proposes two novel tools to automatically perform the orientation and registration of 3D Cone-Beam Computed Tomography (CBCT) scans with high accuracy (<3° and <2mm of angular and linear errors when compared to expert clinicians). These tools have undergone rigorous testing, and are currently being evaluated by clinicians who utilize the 3D Slicer open-source platform. Our work aims to reduce the sources of error in the 3D medical image analysis workflow by automating these operations. These methods combine conventional image processing approaches and Artificial Intelligence (AI) based models trained and tested on de-identified CBCT volumetric images. Our results showed robust performance for standardized and reproducible image orientation and registration that provide a more complete understanding of individual patient facial growth and response to orthopedic treatment in less than 5 min.","PeriodicalId":510889,"journal":{"name":"Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ...","volume":"14242 ","pages":"43-58"},"PeriodicalIF":0.0,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11104011/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141072399","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

An Investigation into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features. 基于乳腺 DCE-MRI 衍生放射组学特征的随机森林模型中种族偏差的研究。

Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ... Pub Date : 2023-01-01 Epub Date: 2023-10-09 DOI: 10.1007/978-3-031-45249-9_22

Mohamed Huti, Tiarna Lee, Elinor Sawyer, Andrew P King

{"title":"An Investigation into Race Bias in Random Forest Models Based on Breast DCE-MRI Derived Radiomics Features.","authors":"Mohamed Huti, Tiarna Lee, Elinor Sawyer, Andrew P King","doi":"10.1007/978-3-031-45249-9_22","DOIUrl":"https://doi.org/10.1007/978-3-031-45249-9_22","url":null,"abstract":"Recent research has shown that artificial intelligence (AI) models can exhibit bias in performance when trained using data that are imbalanced by protected attribute(s). Most work to date has focused on deep learning models, but classical AI techniques that make use of hand-crafted features may also be susceptible to such bias. In this paper we investigate the potential for race bias in random forest (RF) models trained using radiomics features. Our application is prediction of tumour molecular subtype from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) of breast cancer patients. Our results show that radiomics features derived from DCE-MRI data do contain race-identifiable information, and that RF models can be trained to predict White and Black race from these data with 60-70% accuracy, depending on the subset of features used. Furthermore, RF models trained to predict tumour molecular subtype using race-imbalanced data seem to produce biased behaviour, exhibiting better performance on test data from the race on which they were trained.","PeriodicalId":510889,"journal":{"name":"Clinical image-based procedures, fairness of AI in medical imaging, and ethical and philosophical issues in medical imaging : 12th International Workshop, CLIP 2023 1st International Workshop, FAIMI 2023 and 2nd International Workshop, ...","volume":"14242 ","pages":"225-234"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7616589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142485228","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