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检索条件"任意字段=Conference on Medical Imaging: Image Perception, Observer Performance, and Technology Assessment"

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SPIE medical imaging 50th anniversary: history of the image perception, observer performance, and technology assessment conference

SPIE Medical Imaging 50th anniversary: history of the Image ...

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作者： Krupinski, Elizabeth A. Kundel, Harold L. Emory University Department of Radiology and Imaging Sciences AtlantaGA United States University of Pennsylvania Department of Radiology Atrium PhiladelphiaPA United States

As SPIE medical imaging celebrates its 50th anniversary, we reflect on the history of the image perception, observer performance, and technology assessment conference and its importance within the SPIE medical imaging Symposium and the greater medical imaging community. © 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： medical imaging

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medical imaging 2025: image perception, observer performance, and technology assessment

Medical Imaging 2025: Image Perception, Observer Performance...

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medical imaging 2025: image perception, observer performance, and technology assessment

ISBN: (纸本)9781510685963

The proceedings contain 39 papers. The topics discussed include: using gradient of Lagrangian function to compute efficient channels for the ideal observer;effects of feature selection and internal-noise levels for a search-capable model observer;combining image texture and morphological features in low resource perception models for signal detection tasks;perceived color contrast metrics for clinical images;assessment of cell nuclei AI foundation models in kidney pathology;does concurrent reading with ai lead to more false negative errors for cancers that are not marked by AI?;and dual roles of calcification features in the Mirai mammographic breast cancer risk prediction model: early micro-calcification detection and identification of high-risk calcifications.

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Mitigating visual hindsight bias in radiology: Can education alter perceptual decision making?

Mitigating visual hindsight bias in radiology: Can education...

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medical imaging 2025: image perception, observer performance, and technology assessment

作者： Chen, Jacky Reed, Warren Gandomkar, Ziba Medical Image Optimisation and Perception Group Discipline of Medical Imaging Science Faculty of Medicine and Health The University of Sydney SydneyNSW2050 Australia

ISBN: (纸本)9781510685963

This study investigated whether educating radiologists on hindsight bias could mitigate its effects when interpreting chest radiographs containing pulmonary nodules. Sixteen radiologists analysed 15 PA chest X-rays (CXRs) with three levels of lung nodule conspicuity. Initially, they identified nodules by reducing image blurring (foresight phase) and later increased blurring until the nodules became unidentifiable (hindsight phase). After an educational intervention, the experiment was repeated to assess changes in perception. Eye-tracking metrics, including Time to First Fixation (TFF) and Total Fixation Duration (TFD), were measured pre and post intervention. Wilcoxon Signed Rank Tests assessed statistical differences. While a linear mixed-effects model accounted for participant demographics such as specialty, experience and case volume. Results showed a significant change in TFF during the foresight phase (p © 2025 SPIE.

关键词： medical education

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Direct optimization of signal detection metrics in learning-based CT image restoration

Direct optimization of signal detection metrics in learning-...

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medical imaging 2025: image perception, observer performance, and technology assessment

作者： Ongie, Gregory Lantz, Megan Sidky, Emil Y. Reiser, Ingrid S. Pan, Xiaochuan Marquette University MilwaukeeWI United States Department of Radiology University of Chicago ChicagoIL United States

ISBN: (纸本)9781510685963

Convolutional neural networks (CNNs) used for medical image restoration tasks are typically trained by minimizing pixel-wise error metrics, such as mean-squared error (MSE). However, CNNs trained with these losses are prone to wipe-out small/low-contrast features that can be critical for screening and diagnosis. To address this issue, we introduce a novel training loss designed to preserve weak signals in CNN-processed images. The key idea is to measure model observer performance on a user-specified signal detection task implanted in the training data. The proposed loss improves on the recently introduced observer Regularizer (ObsReg) loss,1,2 which is not directly interpretable in terms of signal detection theory and requires specialized training. In contrast, the proposed loss function is defined in directly in terms of a classical signal detection metric, and does not require specialized training. Finally, our experiments on synthetic sparse-view breast CT data show that training a CNN with the proposed loss yields improvement in model observer performance on a signal-known-exactly/background-known-exactly detection task as compared to training with the ObsReg loss. © 2025 SPIE.

关键词： image reconstruction

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Using gradient of Lagrangian function to compute efficient channels for the ideal observer

Using gradient of Lagrangian function to compute efficient c...

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medical imaging 2025: image perception, observer performance, and technology assessment

作者： Zhou, Weimin Department of Medical Imaging University of Arizona Tucson United States Wyant College of Optical Sciences University of Arizona Tucson United States

ISBN: (纸本)9781510685963

It is widely accepted that the Bayesian ideal observer (IO) should be used to guide the objective assessment and optimization of medical imaging systems. The IO employs complete task-specific information to compute test statistics for making inference decisions and performs optimally in signal detection tasks. However, the IO test statistic typically depends non-linearly on the image data and cannot be analytically determined. The ideal linear observer, known as the Hotelling observer (HO), can sometimes be used as a surrogate for the IO. However, when image data are high dimensional, HO computation can be difficult. Efficient channels that can extract task-relevant features have been investigated to reduce the dimensionality of image data to approximate IO and HO performance. This work proposes a novel method for generating efficient channels by use of the gradient of a Lagrangian-based loss function that was designed to learn the HO. The generated channels are referred to as the Lagrangian-gradient (L-grad) channels. Numerical studies are conducted that consider binary signal detection tasks involving various backgrounds and signals. It is demonstrated that channelized HO (CHO) using L-grad channels can produce significantly better signal detection performance compared to the CHO using PLS channels. Moreover, it is shown that the proposed L-grad method can achieve significantly lower computation time compared to the PLS method. © 2025 SPIE.

关键词： Lagrange multipliers

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Interrogating Expert observer performance In A BreastScreen Australia Radiology Cohort

Interrogating Expert Observer Performance In A BreastScreen ...

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medical imaging 2025: image perception, observer performance, and technology assessment

作者： Wells, Jayden B. Trieu, Phuong D. Awwad, Dania Abu Barron, Melissa L. Lewis, Sarah J. Discipline of Medical Imaging Sciences Faculty of Medicine and Health University of Sydney D18 Susan Wakil Health Building Western Avenue CamperdownNSW2006 Australia School of Health Sciences Western Sydney University Locked Bag 1797 PenrithNSW2751 Australia

ISBN: (纸本)9781510685963

Breast cancer has the highest incidence among Australian women, with approximately 20,000 new cases diagnosed in 2022. The national screening program, BreastScreen Australia (BSA), plays a crucial role in early detection, which significantly improves survival rates. Using data from 592 readers on the BreastScreen Reader assessment Strategy (BREAST) platform collected between 2014-2024, this study evaluated the differences in clinical workload and demographic characteristics between BSA readers performing at or above the 95th percentile, compared to that of the general cohort. Furthermore, the impact of cases per week on sensitivity, specificity, lesion localization accuracy, ROC AUC, and JAFROC was considered. It was found that top performing readers had significantly more years in their clinical roles, read more cases per week, and had more experience in mammogram reading. An increased number of cases per week (CPW) was significantly associated with better performance, with a performance plateau observed at approximately 101-150 cases per week. These insights highlight the importance of maintaining reader caseload to achieve optimal screening performance and may inform future guidelines for reader benchmarks and training in the BSA program. © 2025 SPIE.

关键词： Mammography

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Investigating Usable Information for Assessing the Impact of medical image Processing

Investigating Usable Information for Assessing the Impact of...

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medical imaging 2025: image perception, observer performance, and technology assessment

作者： Lu, Changjie Sengupta, Sourya Li, Hua Anastasio, Mark A. Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical & Computer Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Radiation Oncology Washington University St. LouisMO United States Cancer Center at Illinois UrbanaIL United States

ISBN: (纸本)9781510685963

The data processing inequality (DPI) in information theory posits that no data processing can increase the mutual information between data and their task labels. For any post-processing method, the mutual information between post-processed images and task labels should always be less than or, at best, equal to the mutual information between raw images and diagnostic task labels. This is consistent with the fact that the performance of an ideal Bayesian observer cannot be improved through image processing. As such, mutual information is generally not suitable for evaluating the effects of image processing. Recently, a novel variant of mutual information, termed V-information (V-info), has been introduced to account for the computational constraints associated with a sub-ideal observer. In contrast to conventional mutual information, V-info can increase as a result of processing of data, making it a promising task-oriented metric for assessing the impact of image processing. In this study, for the first time, we investigate the application of V-info, which we refer to as the more readily meaningful term"observable-usable information" (O-U-Info), for evaluating the impact of medical image processing. Specifically, we examine deep learning-based super-resolution as the image processing operation. A deep learning-based numerical observer (NO) is employed to perform a Rayleigh binary signal discrimination task using low-resolution, high-resolution, and super-resolved images. We quantify O-U-Info under conditions of varying NO capacity and dataset size. The results demonstrate the potential usefulness of O-U-Info as an objective metric for assessing the impact of medical image processing. © 2025 SPIE.

关键词： Data reduction

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Enhancing Breast Arterial Calcification Segmentation: A Comparative Study of AI and Human Reader Variability

Enhancing Breast Arterial Calcification Segmentation: A Comp...

引用

medical imaging 2025: image perception, observer performance, and technology assessment

作者： Li, Wenbo Yoo, Jay Phil Tao, Yaru Mostafavi, Shiva Zhang, Yumeng Zhang, Qiyu Prasetyo, Chandler Ding, Huanjun Molloi, Sabee Department of Radiological Sciences University of California Irvine United States

ISBN: (纸本)9781510685963

Breast artery calcification (BAC) is linked to a higher risk of cardiovascular diseases and can be detected through mammography. Traditionally, segmentation of BAC by human readers is time-consuming and prone to inter- and intra-observer variability. This study evaluates these variabilities and proposes a novel ensemble deep learning model combining nnU-Net and ResNet152 architectures to reduce them. Results demonstrate strong correlations (R2 = 0.96 among human readers and R2 = 0.97 between the AI and readers) and consistent performance from the AI model, minimizing variability and providing a reliable, standardized approach to BAC segmentation. © 2025 SPIE.

关键词： image segmentation

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Improvement in breast lesion classification utilizing deep learning and treatment response assessment maps (TRAMs)

Improvement in breast lesion classification utilizing deep l...

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medical imaging 2025: image perception, observer performance, and technology assessment

作者： Wang, Jerry Jing, Bowen Fei, Baowei Plano West Senior High School PlanoTX United States Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas Dallas RichardsonTX United States

ISBN: (纸本)9781510685963

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has been widely used for breast lesion diagnosis. However, standard DCE-MRI-based diagnosis has low specificity, leading to unnecessary biopsies and other invasive procedures. A treatment response assessment map (TRAM) involves subtracting the T1-weighted DCE-MRI approximately five minutes after the injection of the contrast agent from a delayed-phase T1-MRI. TRAM characterizes the spatial distribution of contrast accumulation and clearance, potentially aiding in differentiating between benign and malignant lesions. Meanwhile, deep learning-based modeling has shown promising results in many medical imaging diagnostic tasks. In this project, we developed a deep learning model dedicated to breast lesion classification based on TRAM. We used a 3D convolutional residual network (ResNet18) to learn image representations from TRAM. The ResNet18-extracted features were then fed to a fully connected classifier for lesion classification. The TRAM-based model was compared with a model trained on standard multi-phase DCE-MRI. The model trained on TRAM achieved a higher area under the receiver operating characteristic curve (AUROC) (0.870 vs. 0.835), higher sensitivity (0.848 vs. 0.818), and higher specificity (0.823 vs. 0.759) than the model trained on standard DCE-MRI. The presented TRAM-based analyses may be able to aid in the clinical decision-making process during diagnosis and treatment. © 2025 SPIE.

关键词： Dynamic contrast enhanced MRI

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Estimating Task-based performance Bounds for Accelerated MRI image Reconstruction Methods by Use of Learned-Ideal observers

Estimating Task-based Performance Bounds for Accelerated MRI...

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medical imaging 2025: image perception, observer performance, and technology assessment

作者： Li, Kaiyan Li, Hua Myers, Kyle J. Anastasio, Mark A. Department of Bioengineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Radiation Oncology Washington University school of Medicine St. LouisMO United States Cancer Center at Illinois UrbanaIL United States Puente Solutions LLC PhoenixAZ United States

ISBN: (纸本)9781510685963

medical imaging systems are commonly assessed and optimized by the use of objective measures of image quality (IQ). The performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit to guide the optimization of imaging systems. For computed imaging systems, the performance of the IO acting on imaging measurements also sets an upper bound on task-performance that no image reconstruction method can transcend. As such, estimation of IO performance can provide valuable guidance when designing under-sampled data-acquisition techniques by enabling the identification of designs that will not permit the reconstruction of diagnostically inappropriate images for a specified task - no matter how advanced the reconstruction method is or how plausible the reconstructed images appear. The need for such analysis is urgent because of the substantial increase of medical device submissions on deep learning-based image reconstruction methods and the fact that they may produce clean images disguising the potential loss of diagnostic information when data is aggressively under-sampled. Recently, convolutional neural network (CNN) approximated IOs (CNN-IOs) was investigated for estimating the performance of data space IOs to establish task-based performance bounds for image reconstruction, under an X-ray computed tomographic (CT) context. In this work, the application of such data space CNN-IO analysis to multi-coil magnetic resonance imaging (MRI) systems has been explored. This study utilized stylized multi-coil sensitivity encoding (SENSE) MRI systems and deep-generated stochastic brain models to demonstrate the approach. Signal-known-statistically and background-known-statistically (SKS/BKS) binary signal detection tasks were selected to study the impact of different acceleration factors on the data space IO performance. © 2025 SPIE.

关键词： Neuroimaging

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