| 1. |
- Arian, Fatemeh, et al.
(author)
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Myocardial Function Prediction After Coronary Artery Bypass Grafting Using MRI Radiomic Features and Machine Learning Algorithms
- 2022
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In: Journal of digital imaging. - : Springer Nature. - 0897-1889 .- 1618-727X. ; 35:6, s. 1708-1718
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Journal article (peer-reviewed)abstract
- The main aim of the present study was to predict myocardial function improvement in cardiac MR (LGE-CMR) images in patients after coronary artery bypass grafting (CABG) using radiomics and machine learning algorithms. Altogether, 43 patients who had visible scars on short-axis LGE-CMR images and were candidates for CABG surgery were selected and enrolled in this study. MR imaging was performed preoperatively using a 1.5-T MRI scanner. All images were segmented by two expert radiologists (in consensus). Prior to extraction of radiomics features, all MR images were resampled to an isotropic voxel size of 1.8 × 1.8 × 1.8 mm3. Subsequently, intensities were quantized to 64 discretized gray levels and a total of 93 features were extracted. The applied algorithms included a smoothly clipped absolute deviation (SCAD)–penalized support vector machine (SVM) and the recursive partitioning (RP) algorithm as a robust classifier for binary classification in this high-dimensional and non-sparse data. All models were validated with repeated fivefold cross-validation and 10,000 bootstrapping resamples. Ten and seven features were selected with SCAD-penalized SVM and RP algorithm, respectively, for CABG responder/non-responder classification. Considering univariate analysis, the GLSZM gray-level non-uniformity-normalized feature achieved the best performance (AUC: 0.62, 95% CI: 0.53–0.76) with SCAD-penalized SVM. Regarding multivariable modeling, SCAD-penalized SVM obtained an AUC of 0.784 (95% CI: 0.64–0.92), whereas the RP algorithm achieved an AUC of 0.654 (95% CI: 0.50–0.82). In conclusion, different radiomics texture features alone or combined in multivariate analysis using machine learning algorithms provide prognostic information regarding myocardial function in patients after CABG.
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| 2. |
- Bivik Stadler, Caroline, 1986-, et al.
(author)
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Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training
- 2021
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In: Journal of digital imaging. - : Springer-Verlag New York. - 0897-1889 .- 1618-727X. ; 34, s. 105-115
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Journal article (peer-reviewed)abstract
- Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.
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| 3. |
- Cronin, Séan, et al.
(author)
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A Qualitative Analysis of the Needs and Experiences of Hospital-based Clinicians when Accessing Medical Imaging
- 2021
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In: Journal of digital imaging. - : Springer Nature. - 0897-1889 .- 1618-727X. ; :34, s. 385-396
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Journal article (peer-reviewed)abstract
- As digital imaging is now a common and essential tool in the clinical workflow, it is important to understand the experiences of clinicians with medical imaging systems in order to guide future development. The objective of this paper was to explore health professionals’ experiences, practices and preferences when using Picture Archiving and Communications Systems (PACS), to identify shortcomings in the existing technology and inform future developments. Semi-structured interviews are reported with 35 hospital-based healthcare professionals (3 interns, 11 senior health officers, 6 specialist registrars, 6 con- sultants, 2 clinical specialists, 5 radiographers, 1 sonographer, 1 radiation safety officer). Data collection took place between February 2019 and December 2020 and all data are analyzed thematically. A majority of clinicians report using PACS fre- quently (6+ times per day), both through dedicated PACS workstations, and through general-purpose desktop computers. Most clinicians report using basic features of PACS to view imaging and reports, and also to compare current with previous imaging, noting that they rarely use more advanced features, such as measuring. Usability is seen as a problem, including issues related to data privacy. More sustained training would help clinicians gain more value from PACS, particularly less experienced users. While the majority of clinicians report being unconcerned about sterility when accessing digital imaging, clinicians were open to the possibility of touchless operation using voice, and the ability to execute multiple commands with a single voice command would be welcomed.
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| 4. |
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| 5. |
- Dembrower, K, et al.
(author)
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A Multi-million Mammography Image Dataset and Population-Based Screening Cohort for the Training and Evaluation of Deep Neural Networks-the Cohort of Screen-Aged Women (CSAW)
- 2020
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In: Journal of digital imaging. - : Springer Science and Business Media LLC. - 1618-727X .- 0897-1889. ; 33:2, s. 408-413
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Journal article (peer-reviewed)abstract
- For AI researchers, access to a large and well-curated dataset is crucial. Working in the field of breast radiology, our aim was to develop a high-quality platform that can be used for evaluation of networks aiming to predict breast cancer risk, estimate mammographic sensitivity, and detect tumors. Our dataset, Cohort of Screen-Aged Women (CSAW), is a population-based cohort of all women 40 to 74 years of age invited to screening in the Stockholm region, Sweden, between 2008 and 2015. All women were invited to mammography screening every 18 to 24 months free of charge. Images were collected from the PACS of the three breast centers that completely cover the region. DICOM metadata were collected together with the images. Screening decisions and clinical outcome data were collected by linkage to the regional cancer center registers. Incident cancer cases, from one center, were pixel-level annotated by a radiologist. A separate subset for efficient evaluation of external networks was defined for the uptake area of one center. The collection and use of the dataset for the purpose of AI research has been approved by the Ethical Review Board. CSAW included 499,807 women invited to screening between 2008 and 2015 with a total of 1,182,733 completed screening examinations. Around 2 million mammography images have currently been collected, including all images for women who developed breast cancer. There were 10,582 women diagnosed with breast cancer; for 8463, it was their first breast cancer. Clinical data include biopsy-verified breast cancer diagnoses, histological origin, tumor size, lymph node status, Elston grade, and receptor status. One thousand eight hundred ninety-one images of 898 women had tumors pixel level annotated including any tumor signs in the prior negative screening mammogram. Our dataset has already been used for evaluation by several research groups. We have defined a high-volume platform for training and evaluation of deep neural networks in the domain of mammographic imaging.
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| 6. |
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| 7. |
- Geijer, H, et al.
(author)
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Comparison of color LCD and medical-grade monochrome LCD displays in diagnostic radiology.
- 2007
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In: Journal of Digital Imaging. - : Springer Science and Business Media LLC. - 0897-1889 .- 1618-727X. ; 20:2, s. 114-121
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Journal article (peer-reviewed)abstract
- Abstract In diagnostic radiology, medical-grade monochrome displays are usually recommended because of their higher luminance. Standard color displays can be used as a less expensive alternative, but have a lower luminance. The aim of the present study was to compare image quality for these two types of displays. Images of a CDRAD contrast-detail phantom were read by four radiologists using a 2-megapixel (MP) color display (143 cd/m2 maximum luminance) as well as 2-MP (295 cd/m2) and 3-MP monochrome displays. Thirty lumbar spine radiographs were also read by four radiologists using the color and the 2-MP monochrome display in a visual grading analysis (VGA). Very small differences were found between the displays when reading the CDRAD images. The VGA scores were −0.28 for the color and −0.25 for the monochrome display (p=0.24; NS). It thus seems possible to use color displays in diagnostic radiology provided that grayscale adjustment is used. Key words PACS - displays - digital imaging - luminance - image quality - monitor - medical imaging - liquid crystal display
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| 8. |
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| 9. |
- Hassan, A., et al.
(author)
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High Efficiency Video Coding (HEVC)–Based Surgical Telementoring System Using Shallow Convolutional Neural Network
- 2019
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In: Journal of digital imaging. - : Springer Science and Business Media LLC. - 0897-1889 .- 1618-727X. ; 32:6, s. 1027-1043
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Journal article (peer-reviewed)abstract
- Surgical telementoring systems have gained lots of interest, especially in remote locations. However, bandwidth constraint has been the primary bottleneck for efficient telementoring systems. This study aims to establish an efficient surgical telementoring system, where the qualified surgeon (mentor) provides real-time guidance and technical assistance for surgical procedures to the on-spot physician (surgeon). High Efficiency Video Coding (HEVC/H.265)–based video compression has shown promising results for telementoring applications. However, there is a trade-off between the bandwidth resources required for video transmission and quality of video received by the remote surgeon. In order to efficiently compress and transmit real-time surgical videos, a hybrid lossless-lossy approach is proposed where surgical incision region is coded in high quality whereas the background region is coded in low quality based on distance from the surgical incision region. For surgical incision region extraction, state-of-the-art deep learning (DL) architectures for semantic segmentation can be used. However, the computational complexity of these architectures is high resulting in large training and inference times. For telementoring systems, encoding time is crucial; therefore, very deep architectures are not suitable for surgical incision extraction. In this study, we propose a shallow convolutional neural network (S-CNN)–based segmentation approach that consists of encoder network only for surgical region extraction. The segmentation performance of S-CNN is compared with one of the state-of-the-art image segmentation networks (SegNet), and results demonstrate the effectiveness of the proposed network. The proposed telementoring system is efficient and explicitly considers the physiological nature of the human visual system to encode the video by providing good overall visual impact in the location of surgery. The results of the proposed S-CNN-based segmentation demonstrated a pixel accuracy of 97% and a mean intersection over union accuracy of 79%. Similarly, HEVC experimental results showed that the proposed surgical region–based encoding scheme achieved an average bitrate reduction of 88.8% at high-quality settings in comparison with default full-frame HEVC encoding. The average gain in encoding performance (signal-to-noise) of the proposed algorithm is 11.5 dB in the surgical region. The bitrate saving and visual quality of the proposed optimal bit allocation scheme are compared with the mean shift segmentation–based coding scheme for fair comparison. The results show that the proposed scheme maintains high visual quality in surgical incision region along with achieving good bitrate saving. Based on comparison and results, the proposed encoding algorithm can be considered as an efficient and effective solution for surgical telementoring systems for low-bandwidth networks.
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| 10. |
- Kirkhorn, Tomas, et al.
(author)
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Demonstration of digital radiographs by means of ink jet-printed paper copies : Pilot study
- 1992
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In: Journal of Digital Imaging. - 0897-1889. ; 5:4, s. 246-251
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Journal article (peer-reviewed)abstract
- Different digital medical images have been printed on paper with a continuous ink jet printer, and the quality has been evaluated. The emphasis has been on digital chest radiographs from a computed radiography system. The ink jet printing technique is described as well as the handling of the image data from image source to printer. Different versions of paper prints and viewing conditions were compared to find the optimum alternative. The evaluation has been performed to maximize the quality of the paper images to make them conform with the corresponding film prints and monitor images as much as possible. The continuous ink jet technique offers high-quality prints on paper at a considerably lower cost per copy compared with the cost of a film print. With a future switch-over from diagnosing of digital images on film to diagnosing them on monitors, hard copies for demonstration purposes will occasionally be needed. This need can be filled by ink jetprinted paper copies.
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