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In individualized screening mammography, a breast density is important to predict potential risks of breast cancer incidence and missing lesions in mammographic diagnosis. Segmentation of the mammary gland region is required when focusing on missing lesions. A deep-learning method was recently developed to segment the mammary gland region. A large amount of ground truth (prepared by mammary experts) is required for highly accurate deep-learning practice; however, this work is time- and labor-intensive. To streamline the ground truth in deep learning, we investigated a difference in acquired mammary gland regions among multiple radiological technologists having various experience and reading levels, who shared the criteria on segmentation. If we can ignore a skill level for image reading, we can increase a number of training images. Three certified radiological technologists segmented the mammary gland region in 195 mammograms. The degree of coincidence among them was assessed with respect to seven factors which indicated the feature of segmented regions including the breast density and mean glandular dose, using Student’s t-test and Bland-Altman analysis. The assessments made by the three radiological technologists were consistent considering all factors, except the mean pixel value. Thus, we concluded that the ground truths prepared by multiple practitioners with different experiences can be accepted for the segmentation of the mammary gland region and they are applicable for training images if they stringently share the criteria on the segmentation.

This study is aimed to automatically segment mammary gland region into scattered mammary glands and fatty breasts using deep learning method. Total 433 mediolateral oblique-view mammograms of Japanese women were collected and confirmed for scattered mammary glands or fatty breasts; using BI-RADS’s classification. First, manually contoured mammary gland regions were determined for all mammograms as ground truths by three certified radiological technologists. Second, the U-net model was employed to segment the mammary gland region automatically. This model is a type of convolutional neural network (CNN) mainly aimed at medical image segmentation. The segmentation accuracies were assessed based on five criteria, Dice coefficients, breast densities, mean gray values, centroids, and sizes of mammary gland region. The Dice coefficient was 0.915. The mean size of mammary gland regions obtained by the Unet was 8.7% larger than that of the ground truths. The mean centroid coordinates of mammary gland regions by the U-net were shifted 1.6 and 5.4 mm on average in mediolateral and craniocaudal directions, respectively from ground truths. The mean gray value of mammary gland regions obtained by the U-net was only 0.4% higher compared with ground truths. The resultant difference was 0.4% on average in breast densities between ground truths and the segmented mammary gland regions. We found significant similarity in the ground truths and the data generated by deep learning on all the parameters, thereby attesting the efficacy of this method for segmenting the mammary gland regions of not only the dense breasts but also the scattered mammary gland- and fatty- breasts.

Receiver operating characteristic (ROC) examination was performed to investigate the effectiveness of high-luminance monitors in digital X-ray mammography. For this purpose, an original breast phantom consisting of adipose and fibroglandular equivalent tissues with an identical X-ray absorption characteristic over the entire mammographic photon energy range was developed. Furthermore, the phantom’s fibroglandular density and distribution could be changed arbitrarily. Three types of lesions, microcalcification, mass, and spiculated, were inserted into the breast phantom, and the ROC examination was performed by five radiological technologists certified in screening mammography, to obtain the area under the curve. A liquid crystal display (LCD) monitor with 5 megapixels in a 21-inch display size calibrated to a grayscale standard display function curve was used for the observation. The monitor was set to 600, 900, and 1200 cd/m2 in maximum luminance. The experimental details were fibroglandular density of 25%, respective 50 positive and negative images, and free observation time and distance. As a result, the dependence on monitor luminance differed according to the lesion type. The detectability of microcalcification increased with the increase in the luminance of the monitor. Spiculated lesions were similar for all luminance changes. The detectability of mass lesions was significantly higher at 900 cd/m2 than at 600 cd/m2 . There was no significant difference between those at 900 cd/m2 and 1200 cd/m2 . In conclusion, the maximum luminance of the diagnostic LCD monitor for mammography should be at least 900 cd/m2 to guarantee stable detectability.

Recently, digital mammography with a photon counting silicon detector has been developed. With the aim of reducing the exposure dose, we have proposed a new mammography system that uses a cadmium telluride series photon counting detector. In addition, we also propose to use a high energy X-ray spectrum with a tungsten anode. The purpose of this study was assessed that the effectiveness of the high X-ray energy spectrum in terms of image quality using a Monte Carlo simulation. The proposed photon counting system with the high energy X-ray is compared to a conventional flat panel detector system with a Mo/Rh spectrum. The contrast-to-noise ratio (CNR) is calculated from simulation images with the use of breast phantoms. The breast model phantoms differed by glandularity and thickness, which were determined from Japanese clinical mammograms. We found that the CNR values were higher in the proposed system than in the conventional system. The number of photons incident on the detector was larger in the proposed system, so that the noise values was lower in comparison with the conventional system. Therefore, the high energy spectrum yielded the same CNR as using the conventional spectrum while allowing a considerable dose reduction to the breast.

We propose a new method for reduction of the patient dose by using scattered X-rays in order to achieve the same density without scattered rays. The minimum perceptible thickness difference (delta) Xmin of the object was calculated using psychophysical analysis for various radiographic densities, scatter fractions and luminous exitances of the viewer. The mAs values to obtain many densities were measured using four kinds of anti-scattered X-ray grid with their scatter fractions. These measured values were applied to above calculated psychophysical results. The smallest value of (delta) Xmin for acrylate of thickness 20cm was 0.18mm, if the scattered X-rays were negligible. The value of (delta) Xmin increases with increasing scatter fraction. The perceptibility of human eye is influenced by luminous exitance of the viewer. By increasing the luminous exitance from 1500 lm(DOT)m-2 to 8000 lm(DOT)m-2, the patient dose can be reduced 33 percent in maximum under the same perceptibility of (delta) Xmin. The method of changing grid will be considered.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.