How does image quality affect radiologists’ perceived ability for image interpretation and lesion detection in digital mammography?

Boita, Joana; Engen, Ruben E. van; Mackenzie, Alistair; Tingberg, Anders; Bosmans, Hilde; Bolejko, Anetta; Zackrisson, Sophia; Wallis, Matthew; Ikeda, Debra M.; Ongeval, Chantal Van; Pijnappel, Ruud M.; Broeders, Mireille J. M.; Sechopoulos, Ioannis

doi:10.1007/s00330-020-07679-8

Cited by 18 publications

(20 citation statements)

References 25 publications

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“…A higher J or IoU score brings better similarity between the two sets. The accuracy of the segmentation was measured based on its testing performance on different input image settings, based on Equation (11), utilizing TP, FP, TN, and FN.…”

Section: Mass Segmentation and Evaluationmentioning

confidence: 99%

“…Past studies concluded that mass detection decreased with increased density, due to the mass itself being similar to the surrounding dense tissue of the breast [8][9][10]. Additionally, image quality conditions also make it difficult to detect the lesion in dense breasts [11,12]. Specifying the edge of the mass from its surrounding dense tissue requires image processing that enhances the textural element of the image as one of the defining mass descriptors to assess a mammogram visually [13].…”

Section: Introductionmentioning

confidence: 99%

“…Its refinement aims to go beyond human-eye perception by defining semantic descriptors to extract quantitative radiological data [14]. To accommodate the analysis of mammographic mass, Computer-Aided Diagnosis (CAD) systems are introduced to breast cancer diagnosis stages, from improving the image quality [11,15], breast lesion detection, and segmentation [16], as well as benign or malignant classification [16][17][18][19]. Moreover, CAD implementation in mammography diagnostic could reduce the human rater's false-positive rate by 5.7% and false negative by 9.4%, as shown in a USA-based dataset [19], and an increase rate of 3% recall rate for a radiologist's mammogram analysis with CAD assistance for an expert radiologist [20].…”

Section: Introductionmentioning

confidence: 99%

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Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis

et al. 2023

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Mass detection in mammograms has a limited approach to the presence of a mass in overlapping denser fibroglandular breast regions. In addition, various breast density levels could decrease the learning system’s ability to extract sufficient feature descriptors and may result in lower accuracy performance. Therefore, this study is proposing a textural-based image enhancement technique named Spatial-based Breast Density Enhancement for Mass Detection (SbBDEM) to boost textural features of the overlapped mass region based on the breast density level. This approach determines the optimal exposure threshold of the images’ lower contrast limit and optimizes the parameters by selecting the best intensity factor guided by the best Blind/Reference-less Image Spatial Quality Evaluator (BRISQUE) scores separately for both dense and non-dense breast classes prior to training. Meanwhile, a modified You Only Look Once v3 (YOLOv3) architecture is employed for mass detection by specifically assigning an extra number of higher-valued anchor boxes to the shallower detection head using the enhanced image. The experimental results show that the use of SbBDEM prior to training mass detection promotes superior performance with an increase in mean Average Precision (mAP) of 17.24% improvement over the non-enhanced trained image for mass detection, mass segmentation of 94.41% accuracy, and 96% accuracy for benign and malignant mass classification. Enhancing the mammogram images based on breast density is proven to increase the overall system’s performance and can aid in an improved clinical diagnosis process.

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Section: Mass Segmentation and Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis

et al. 2023

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“…These factors are in turn altered by parameters related to the image acquisition process, such as breast compression and thickness, which aims at minimizing scattered radiation and thus increasing contrast and SNR, positioning, the x-ray beam quality (determined by the anode/filter combination and voltage), and the radiation dose. 7,8 Furthermore, these parameters may affect differently in the detection of different findings suggesting breast cancer, which are mainly soft-tissue lesions and calcifications. 9,10 In the case of the former, the margins delimiting the lesion and its density are determinants of its malignancy, whereas in the case of calcifications, their number, morphology and distribution predominate.…”

mentioning

confidence: 99%

“…9,10 In the case of the former, the margins delimiting the lesion and its density are determinants of its malignancy, whereas in the case of calcifications, their number, morphology and distribution predominate. 8 Thus, factors, such as contrast, may affect more severely the detection of soft-tissue lesions, whereas spatial resolution or noise may be more decisive in detecting calcifications. 11,12 In addition, breast density (i.e., the percentage or absolute amount of fibroglandular tissue) is also known for being an essential factor affecting image quality.…”

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confidence: 99%

Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection

et al. 2023

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Purpose: Population-based screening programs for the early detection of breast cancer have significantly reduced mortality in women, but they are resource intensive in terms of time, cost, and workload and still have limitations mainly due to the use of 2D imaging techniques, which may cause overlapping of tissues, and interobserver variability. Artificial intelligence (AI) systems may be a valuable tool to assist radiologist when reading and classifying mammograms based on the malignancy of the detected lesions. However, there are several factors that can influence the outcome of a mammogram and thus also the detection capability of an AI system. The aim of our work is to analyze the robustness of the diagnostic ability of an AI system designed for breast cancer detection.Approach: Mammograms from a population-based screening program were scored with the AI system. The sensitivity and specificity by means of the area under the receiver operating characteristic (ROC) curve were obtained as a function of the mammography unit manufacturer, demographic characteristics, and several factors that may affect the image quality (age, breast thickness and density, compression applied, beam quality, and delivered dose). Results:The area under the curve (AUC) from the scoring ROC curve was 0.92 (95% confidence interval = 0.89 − 0.95). It showed no dependence with any of the parameters considered, as the differences in the AUC for different interval values were not statistically significant. Conclusion:The results suggest that the AI system analyzed in our work has a robust diagnostic capability, and that its accuracy is independent of the studied parameters.

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The impact of (simulated) resolution on breast cancer diagnosis based on high‐resolution 3D micro‐CT microcalcification images

Brahimetaj,

Cornelis,

Van Ongeval

et al. 2023

Medical Physics

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BackgroundBreast microcalcifications (MCs) are considered to be a robust marker of breast cancer. A machine learning model can provide breast cancer diagnosis based on properties of individual MCs ‐ if their characteristics are captured at high resolution and in 3D.PurposeThe main purpose of the study was to explore the impact of image resolution (8 µm, 16 µm, 32 µm, 64 µm) when diagnosing breast cancer using radiomics features extracted from individual high resolution 3D micro‐CT MC images.MethodsBreast MCs extracted from 86 female patients were analyzed at four different spatial resolutions: 8 µm (original resolution) and 16 µm, 32 µm, 64 µm (simulated image resolutions). Radiomic features were extracted at each image resolution in an attempt, to find a compact feature signature allowing to distinguish benign and malignant MCs. Machine learning algorithms were used for classifying individual MCs and samples (i.e., patients). For sample diagnosis, a custom‐based thresholding approach was used to combine individual MC results into sample results. We conducted classification experiments when using (a) the same MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution; (b) the same MCs visible in 8 µm, 16 µm, and 32 µm resolution; (c) the same MCs visible in 8 µm and 16 µm resolution; (d) all MCs visible in 8 µm, 16 µm, 32 µm, and 64 µm resolution. Accuracy, sensitivity, specificity, AUC, and F1 score were computed for each experiment.ResultsThe individual MC results yielded an accuracy of 77.27%, AUC of 83.83%, F1 score of 77.25%, sensitivity of 80.86%, and specificity of 72.2% at 8 µm resolution. For the individual MC classifications we report for the F1 scores: a 2.29% drop when using 16 µm instead of 8 µm, a 4.01% drop when using 32 µm instead of 8 µm, a 10.69% drop when using 64 µm instead of 8 µm. The sample results yielded an accuracy and F1 score of 81.4%, sensitivity of 80.43%, and specificity value of 82.5% at 8 µm. For the sample classifications we report for F1 score values: a 6.3% drop when using 16 µm instead of 8 µm, a 4.91% drop when using 32 µm instead of 8 µm, and a 6.3% drop when using 64 µm instead of 8 µm.ConclusionsThe highest classification results are obtained at the highest resolution (8 µm). If breast MCs characteristics could be visualized/captured in 3D at a higher resolution compared to what is used nowadays in digital mammograms (approximately 70 µm), breast cancer diagnosis will be improved.

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How does image quality affect radiologists’ perceived ability for image interpretation and lesion detection in digital mammography?

Cited by 18 publications

References 25 publications

Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis

Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis

Multi-vendor robustness analysis of a commercial artificial intelligence system for breast cancer detection

The impact of (simulated) resolution on breast cancer diagnosis based on high‐resolution 3D micro‐CT microcalcification images

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