Purpose: To apply a convolutional neural network (CNN) to develop a system that segments intensity calibration phantom regions in computed tomography (CT) images, and to test the system in a large cohort to evaluate its robustness. Methods: A total of 1040 cases (520 cases each from two institutions), in which an intensity calibration phantom (B-MAS200, Kyoto Kagaku, Kyoto, Japan) was used, were included herein. A training dataset was created by manually segmenting the regions of the phantom for 40 cases (20 cases each). Segmentation accuracy of the CNN model was assessed with the Dice coefficient and the average symmetric surface distance (ASD) through the 4-fold cross validation. Further, absolute differences of radiodensity values (in Hounsfield units: HU) were compared between manually segmented regions and automatically segmented regions. The system was tested on the remaining 1000 cases. For each institution, linear regression was applied to calculate coefficients for the correlation between radiodensity and the densities of the phantom. Results: After training, the median Dice coefficient was 0.977, and the median ASD was 0.116 mm. When segmented regions were compared between manual segmentation and automated segmentation, the median absolute difference was 0.114 HU. For the test cases, the median correlation coefficient was 0.9998 for one institution and was 0.9999 for the other, with a minimum value of 0.9863. Conclusions: The CNN model successfully segmented the calibration phantom's regions in the CT images with excellent accuracy, and the automated method was found to be at least equivalent to the conventional manual method. Future study should integrate the system by automatically segmenting the region of interest in bones such that the bone mineral density can be fully automatically quantified from CT images.
Nanchang University（南昌大学）；Paul C. Lauterbur Research Center for Biomedical Imaging, SIAT, CAS（中国科学院深圳先进技术研究院保罗·C·劳特伯生物医学成像研究中心）
The integration of compressed sensing and parallel imaging (CS-PI) provides a robust mechanism for accelerating MRI acquisitions. However, most such strategies require the explicit formation of either coil sensitivity profiles or a cross-coil correlation operator, and as a result reconstruction corresponds to solving a challenging bilinear optimization problem. In this work, we present an unsupervised deep learning framework for calibration-free parallel MRI, coined universal generative modeling for parallel imaging (UGM-PI). More precisely, we make use of the merits of both wavelet transform …
ImFusion GmbH（ImFusion有限公司）；Technische Universität München（慕尼黑工业大学）
We propose a novel method to automatically calibrate tracked ultrasound probes. To this end we design a custom phantom consisting of nine cones with different heights. The tips are used as key points to be matched between multiple sweeps. We extract them using a convolutional neural network to segment the cones in every ultrasound frame and then track them across the sweep. The calibration is robustly estimated using RANSAC and later refined employing image based techniques. Our phantom can be 3D-printed and offers many advantages over state-of-the-art methods. The phantom design and algorithm…
The use of computed tomography (CT) imaging has become of increasing interest to academic areas outside of the field of medical imaging and industrial inspection, e.g., to biology and cultural heritage research. The pecularities of these fields, however, sometimes require that objects need to be imaged on-site, e.g., in field-work conditions or in museum collections. Under these circumstances, it is often not possible to use a commercial device and a custom solution is the only viable option. In order to achieve high image quality under adverse conditions, reliable calibration and trajectory r…
Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or digital-twins (DT) using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registra…
Calibrated Uncertainty for Trustworthy Clinical Gait Analysis Using Probabilistic Multiview Markerless Motion Capture
Shriners Children’s Lexington（夏皮尔儿童医院莱克星顿分院）；Dept. of Physical Therapy, University of Kentucky（肯塔基大学物理治疗系）；Shirley Ryan AbilityLab（希拉里·瑞安能力实验室）；Northwestern University（西北大学）；University of Göttingen（哥廷根大学）；Lower Saxony Center for AI & Causal Methods in Medicine（下萨克森人工智能与因果方法医学中心）；Shriners Children’s Philadelphia（夏皮尔儿童医院费城分院）
Auditing Multimodal LLM Raters: Central Tendency Bias in Clinical Ordinal Scoring
Department of Electrical & Computer Engineering（电气与计算机工程系）；Department of Computer and Information Science and Engineering（计算机与信息科学与工程系）；Department of Clinical and Health Psychology（临床与健康心理学系）；Department of Biomedical Engineering（生物医学工程系）
Duke University School of Medicine（杜克大学医学院）；Duke University（杜克大学）；National Artificial Intelligence Institute, U.S. Dept. of Veterans Affairs（美国退伍军人事务部国家人工智能研究所）
Research studies of artificial intelligence models in medical imaging have been hampered by poor generalization. This problem has been especially concerning over the last year with numerous applications of deep learning for COVID-19 diagnosis. Virtual imaging trials (VITs) could provide a solution for objective evaluation of these models. In this work utilizing the VITs, we created the CVIT-COVID dataset including 180 virtually imaged computed tomography (CT) images from simulated COVID-19 and normal phantom models under different COVID-19 morphology and imaging properties. We evaluated the pe…
When Measurement Conventions Masquerade as Calibration Gains in Cardiac Digital Twins
College of Engineering and Computer Science, VinUniversity（文朗大学工程与计算机科学学院）；VinUniversity（文朗大学）；Center for Innovations in Health Sciences, VinUniversity（文朗大学健康科学创新中心）
Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction
Inria；University of Strasbourg（斯特拉斯堡大学）；CNRS（法国国家科学研究中心）；INSERM（法国国家医学研究院）；ICube（ICube研究中心）；UMR7357（UMR7357研究单位）；University of Basel（巴塞尔大学）；Department of Biomedical Engineering（生物医学工程系）；Institute of Image-Guided Surgery（影像引导手术研究所）；IHU Strasbourg（斯特拉斯堡IHU）
East China Normal University（华东师范大学）；Huashan Hospital, Fudan University（复旦大学附属华山医院）
Medical image segmentation is one of the fundamental problems for artificial intelligence-based clinical decision systems. Current automatic medical image segmentation methods are often failed to meet clinical requirements. As such, a series of interactive segmentation algorithms are proposed to utilize expert correction information. However, existing methods suffer from some segmentation refining failure problems after long-term interactions and some cost problems from expert annotation, which hinder clinical applications. This paper proposes an interactive segmentation framework, called inte…
Morphological Decoupling-Based Skeletal Classification for Clinical Assessment of Malocclusion
The Affiliated Stomatological Hospital of Nanjing Medical University（南京医科大学附属口腔医院）；Nanjing Medical University（南京医科大学）；Jiangsu Province Engineering Research Center of Stomatological Translational Medicine（江苏省口腔转化医学工程研究中心）；The Chinese University of Hong Kong（香港中文大学）
Microsoft Research（微软研究院）；University of Pennsylvania（宾夕法尼亚大学）
Accelerated MRI reconstructs images of clinical anatomies from sparsely sampled signal data to reduce patient scan times. While recent works have leveraged deep learning to accomplish this task, such approaches have often only been explored in simulated environments where there is no signal corruption or resource limitations. In this work, we explore augmentations to neural network MRI image reconstructors to enhance their clinical relevancy. Namely, we propose a ConvNet model for detecting sources of image artifacts that achieves a classifier $F_2$ score of 79.1%. We also demonstrate that tra…
Università degli Studi di Milano（米兰大学）；INSTM RU（意大利国家物质结构研究所研究单元）；Champalimaud Foundation（尚帕利莫基金会）；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（高等技术学院）；Philips Healthcare（飞利浦医疗保健）；Università degli Studi di Pavia（帕维亚大学）
In this study we investigated the repeatability and reproducibility of radiomic features extracted from MRI images and provide a workflow to identify robust features. 2D and 3D T$_2$-weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of the radiomic features were assessed respectively by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC), considering repeated acquisitions with or without phantom repositioning, and with different scanner/acquisition t…
We hypothesize that probabilistic voxel-level classification of anatomy and malignancy in prostate MRI, although typically posed as near-identical segmentation tasks via U-Nets, require different loss functions for optimal performance due to inherent differences in their clinical objectives. We investigate distribution, region and boundary-based loss functions for both tasks across 200 patient exams from the publicly-available ProstateX dataset. For evaluation, we conduct a thorough comparative analysis of model predictions and calibration, measured with respect to multi-class volume segmentat…
University of Cambridge（剑桥大学）；Imperial College London（帝国理工学院）；University of Salerno（萨勒诺大学）
The Dice similarity coefficient (DSC) is both a widely used metric and loss function for biomedical image segmentation due to its robustness to class imbalance. However, it is well known that the DSC loss is poorly calibrated, resulting in overconfident predictions that cannot be usefully interpreted in biomedical and clinical practice. Performance is often the only metric used to evaluate segmentations produced by deep neural networks, and calibration is often neglected. However, calibration is important for translation into biomedical and clinical practice, providing crucial contextual infor…
Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
Karolinska Institutet（卡罗林斯卡学院）；FFEI Ltd（FFEI有限公司）；University of Turku（图尔库大学）；Stavanger University Hospital（斯塔万格大学医院）；University of Stavanger（斯塔万格大学）；Aarhus University Hospital（奥胡斯大学医院）；Aarhus University（奥胡斯大学）；Tampere University（坦佩雷大学）
Data Alchemy: Mitigating Cross-Site Model Variability Through Test Time Data Calibration
Children’s National Hospital, Washington, DC, USA（儿童医院国家医院）；Universidad Politécnica de Madrid, Madrid, Spain（马德里理工大学）；Universitat Pompeu Fabra, Barcelona, Spain（庞培法布拉大学）；Nvidia Corporation, Santa Clara, CA, USA（英伟达公司）；George Washington University, Washington, DC, USA（乔治华盛顿大学）
