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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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Special Issue Editorial: medical image perception and Observer Performance

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JOURNAL OF medical imaging 2023年第1期10卷

作者： Evans, Karla Kumcu, Asli Krupinski, Elizabeth A. Univ York Dept Psychol York England Univ Ghent TELIN Dept Ghent Belgium Emory Univ Dept Radiol & Imaging Sci Atlanta GA 30322 USA

The editorial introduces the JMI Special Section on medical image perception and Observer Performance.

关键词： medical imaging Education and training Mammography Visualization image quality image processing Radiology Diagnostics medical research Performance modeling

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Perceived color contrast metrics for clinical images

Perceived color contrast metrics for clinical images

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2025 conference on medical imaging

作者： De Vylder, Jonas Ouillette, Peter Diricx, Bart Rostang, Johan Kimpe, Tom Yousif, Mustafa Barco NV Beneluxpk 21 Kortrijk Belgium Michigan Med Ann Arbor MI USA

ISBN: (纸本)9781510685963;9781510685970

In medical imaging, contrast plays a crucial role in determining visual quality and facilitating accurate interpretation, particularly in fields like digital pathology and dermatology where color variations are diagnostically significant. Traditional contrast metrics often focus on intensity variations, potentially overlooking critical color information. This study introduces two novel color contrast metrics designed to quantify color variations at the image level, addressing this gap. The proposed metrics are a generalization of histogram-based methods where perceptual color difference metrics are used to calculate maximum and spread contrast measures. Experimental evaluation, including a study with 28 volunteers assessing image pairs, demonstrated a high correlation (Cohen's kappa of 0.92) between these metrics and human-perceived color contrast. Furthermore, statistical analyses confirmed that these metrics reliably distinguish color differences beyond luminance variations. The metrics proved robust across different parameter settings, as demonstrated by stability tests. Additionally, practical applications were explored in the field of digital pathology. The study concludes that these color contrast metrics align well with human perception and offer a valuable tool for enhancing diagnostic accuracy in medical imaging.

关键词： perceived color variation observer study digital pathology no-reference image quality assessment

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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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2025 conference on medical imaging

作者： Chen, Jacky Reed, Warren Gandomkar, Ziba Univ Sydney Med Image Optimisat & Percept Grp Discipline Med Imaging Sci Fac Med & Hlth Sydney NSW 2050 Australia

ISBN: (纸本)9781510685963;9781510685970

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<0.001) but no change in TFD. Indicating that hindsight bias influenced fixation patterns. In the hindsight phase, TFD significantly changed post-intervention, suggesting that education altered visual search behaviours. Higher lesion conspicuity increased the impact of education on TFD (p=0.03), while years of experience (p=0.03) and thoracic radiology specialisation (p=0.02) reduced this effect. No significant changes were found for TFF. These findings suggest that education can mitigate hindsight bias in radiology by altering search behaviours in the hindsight phase, though its impact varies by experience and speciality. This may have implications for radiology training and expert witness evaluations in medicolegal cases.

关键词： Visual hindsight bias image perception cognitive bias mitigation radiologist education

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Combining image Texture and Morphological Features in Low-Resource perception Models for Signal Detection Tasks

Combining Image Texture and Morphological Features in Low-Re...

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2025 conference on medical imaging

作者： Das, Mini Lin, Hongwei Andrade, Diego Gifford, Howard C. Univ Houston Dept Biomed Engn Houston TX 77004 USA Univ Houston Dept Phys Houston TX 77204 USA Univ Houston Dept Elect & Comp Engn Houston TX 77204 USA

ISBN: (纸本)9781510685963;9781510685970

Texture analysis holds significant importance in various imaging fields due to its ability to provide statistical, structural, and intrinsic spatial information from images. In this work, we examine several first and second-order texture features on simulated and clinical DBT images. We examined some essential characteristics of texture features that show higher discriminatory potential for mass detection in digital breast tomosynthesis. We further examined the use of these texture features along with morphological features in a two stage visual search (VS) model observer for mass detection in DBT. Our preliminary results show that incorporation of texture features reduced the number of suspicious locations in the first stage of VS model. Our preliminary results with an eye tracking system and observer gaze points align well with the "search" regions predicted by either the texture aided or thresholded VS observer. In summary, we show how additing perceptually relevant texture features or a thresholding mechanism enhances our visual search observer models. Future work will examine feature selections for changing tasks.

关键词： Accuracy classification clustering GLCM GMM perception segmentation texture feature maps visual search model observer

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Ambient Denoising Diffusion Generative Adversarial Networks for Establishing Stochastic Object Models from Noisy image Data

Ambient Denoising Diffusion Generative Adversarial Networks ...

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2025 conference on medical imaging

作者： Xu, Xichen Chen, Wentao Zhou, Weimin Shanghai Jiao Tong Univ Global Inst Future Technol Shanghai 200240 Peoples R China Univ Michigan Ann Arbor MI USA Shanghai Jiao Tong Univ Joint Inst Shanghai 200240 Peoples R China Univ Arizona Dept Med Imaging Tucson AZ 85721 USA Univ Arizona Wyant Coll Optic Sci Tucson AZ 85721 USA

ISBN: (纸本)9781510685963;9781510685970

It is widely accepted that medical imaging systems should be objectively assessed via task-based image quality (IQ) measures that ideally account for all sources of randomness in the measured image data, including the variation in the ensemble of objects to be imaged. Stochastic object models (SOMs) that can randomly draw samples from the object distribution can be employed to characterize object variability. To establish realistic SOMs for task-based IQ analysis, it is desirable to employ experimental image data. However, experimental image data acquired from medical imaging systems are subject to measurement noise. Previous work investigated the ability of deep generative models (DGMs) that employ an augmented generative adversarial network (GAN), AmbientGAN, for establishing SOMs from noisy measured image data. Recently, denoising diffusion models (DDMs) have emerged as a leading DGM for image synthesis and can produce superior image quality than GANs. However, original DDMs possess a slow image-generation process because of the Gaussian assumption in the denoising steps. More recently, denoising diffusion GAN (DDGAN) was proposed to permit fast image generation while maintain high generated image quality that is comparable to the original DDMs. In this work, we propose an augmented DDGAN architecture, Ambient DDGAN (ADDGAN), for learning SOMs from noisy image data. Numerical studies that consider clinical computed tomography (CT) images and digital breast tomosynthesis (DBT) images are conducted. The ability of the proposed ADDGAN to learn realistic SOMs from noisy image data is demonstrated. It has been shown that the ADDGAN significantly outperforms the advanced AmbientGAN models for synthesizing high resolution medical images with complex textures.

关键词： Stochastic object model deep generative model denoising diffusion model generative adversarial network

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Task-based Regularization in Penalized Least-Squares for Binary Signal Detection Tasks in medical image Denoising

Task-based Regularization in Penalized Least-Squares for Bin...

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2025 conference on medical imaging

作者： Chen, Wentao Xu, Tianming Zhou, Weimin Univ Michigan Ann Arbor MI 48109 USA Shanghai Jiao Tong Univ Joint Inst Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Global Inst Future Technol Shanghai 200240 Peoples R China Univ Arizona Dept Med Imaging Tucson AZ 85721 USA Univ Arizona Wyant Coll Optic Sci Tucson AZ 85721 USA

ISBN: (纸本)9781510685963;9781510685970

image denoising algorithms have been extensively investigated for medical imaging. To perform image denoising, penalized least-squares (PLS) problems can be designed and solved, in which the penalty term encodes prior knowledge of the object being imaged. Sparsity-promoting penalties, such as total variation (TV), have been a popular choice for regularizing image denoising problems. However, such hand-crafted penalties may not be able to preserve task-relevant information in measured image data and can lead to oversmoothed image appearances and patchy artifacts that degrade signal detectability. Supervised learning methods that employ convolutional neural networks (CNNs) have emerged as a popular approach to denoising medical images. However, studies have shown that CNNs trained with loss functions based on traditional image quality measures can lead to a loss of task-relevant information in images. Some previous works have investigated task-based loss functions that employ model observers for training the CNN denoising models. However, such training processes typically require a large number of noisy and ground-truth (noise-free or low-noise) image data pairs. In this work, we propose a task-based regularization strategy for use with PLS in medical image denoising. The proposed task-based regularization is associated with the likelihood of linear test statistics of noisy images for Gaussian noise models. The proposed method does not require ground-truth image data and solves an individual optimization problem for denoising each image. Computer-simulation studies are conducted that consider a multivariate-normally distributed (MVN) lumpy background and a binary texture background. It is demonstrated that the proposed regularization strategy can effectively improve signal detectability in denoised images.

关键词： Penalized least-squares medical image denoising total variation observer model

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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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2025 conference on medical imaging

作者： Zhou, Weimin Univ Arizona Dept Med Imaging Tucson AZ 85721 USA Univ Arizona Wyant Coll Opt Sci Tucson AZ USA

ISBN: (纸本)9781510685963;9781510685970

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.

关键词： Efficient channels ideal observer Lagrangian function dimensionality reduction

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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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2025 conference on medical imaging

作者： Li, Kaiyan Li, Hua Myers, Kyle J. Anastasio, Mark A. Univ Illinois Dept Bioengn Urbana IL 61801 USA Washington Univ Dept Radiat Oncol Sch Med St Louis MO USA Canc Ctr Illinois Urbana IL USA Puente Solut LLC Phoenix AZ USA

ISBN: (纸本)9781510685963;9781510685970

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.

关键词： image restoration Magnetic resonance imaging Information operations Signal detection imaging systems Binary data Brain Diffusion Stochastic processes Visualization

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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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2025 conference on medical imaging

作者： Ongie, Gregory Lantz, Megan Sidky, Emil Y. Reiser, Ingrid S. Pan, Xiaochuan Marquette Univ Mathemat & Stat Sci Milwaukee WI 53233 USA Univ Chicago Dept Radiol Chicago IL USA

ISBN: (纸本)9781510685963;9781510685970

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.

关键词： image restoration deep learning image quality assessment model observers breast CT convolutional neural networks

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