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Multimedia, including audio, image, and video, etc., is a ubiquitous part of modern life. Quality evaluation, both objective and subjective, is of fundamental importance for various multimedia applications. In this letter, a novel quality-aware feature is proposed for blind/no-reference (NR) image quality assessment (IQA). The new quality-aware feature is generated from the proposed joint generalized local binary pattern (GLBP) statistics. In this method, using the Laplacian of Gaussian (LOG) filters, the images are first decomposed into multi-scale subband images. Then, the subband images are encoded with the proposed GLBP operator and the quality-aware features are formed from the joint GLBP histograms from the encoding maps of each subband image. Finally, using support vector regression (SVR), the quality-aware features are mapped to the image's subjective quality score for NR-IQA. The experimental results for two representative databases show that the proposed method is strongly correlated to subjective quality evaluations and competitive to the state-of-the-art NR-IQA methods. [ABSTRACT FROM AUTHOR]

The detection of microaneurysms in digital color fundus photographs is a critical first step in automated screening for diabetic retinopathy (DR), a common complication of diabetes. To accomplish this detection numerous methods have been published in the past but none of these was compared with each other on the same data. In this work we present the results of the first international microaneurysm detection competition, organized in the context of the Retinopathy Online Challenge (ROC), a multiyear online competition for various aspects of DR detection. For this competition, we compare the results of five different methods, produced by five different teams of researchers on the same set of data. The evaluation was performed in a uniform manner using an algorithm presented in this work. The set of data used for the competition consisted of 50 training images with available reference standard and 50 test images where the reference standard was witheld by the organizers (M. Niemeijer, B. van Ginneken, and M. D. Abràmoff). The results obtained on the test data was submitted through a website after which standardized evaluation software was used to determine the performance of each of the methods. A human expert detected microaneurysms in the test set to allow comparison with the performance of the automatic methods. The overall results show that microaneurysm detection is a challenging task for both the automatic methods as well as the human expert. There is room for improvement as the best performing system does not reach the performance of the human expert. The data associated with the ROC microaneurysm detection competition will remain publicly available and the website will continue accepting submissions. [ABSTRACT FROM AUTHOR]

The presentation of images with lesions of known pathology that are similar to an unknown lesion may be helpful to radiologists in the diagnosis of challenging cases for improving the diagnostic accuracy and also for reducing variation among different radiologists. The authors have been developing a computerized scheme for automatically selecting similar images with clustered microcalcifications on mammograms from a large database. For similar images to be useful, they must be similar from the point of view of the diagnosing radiologists. In order to select such images, subjective similarity ratings were obtained for a number of pairs of clustered microcalcifications by breast radiologists for establishment of a “gold standard” of image similarity, and the gold standard was employed for determination and evaluation of the selection of similar images. The images used in this study were obtained from the Digital Database for Screening Mammography developed by the University of South Florida. The subjective similarity ratings for 300 pairs of images with clustered microcalcifications were determined by ten breast radiologists. The authors determined a number of image features which represent the characteristics of clustered microcalcifications that radiologists would use in their diagnosis. For determination of objective similarity measures, an artificial neural network (ANN) was employed. The ANN was trained with the average subjective similarity ratings as teacher and selected image features as input data. The ANN was trained to learn the relationship between the image features and the radiologists’ similarity ratings; therefore, once the training was completed, the ANN was able to determine the similarity, called a psychophysical similarity measure, which was expected to be close to radiologists’ impressions, for an unknown pair of clustered microcalcifications. By use of a leave-one-out test method, the best combination of features was selected. The correlation coefficient between the gold standard and the psychophysical similarity measure through the use of seven features was relatively high (r=0.71) and was comparable to the correlation coefficients between the ratings by one radiologist and the average ratings by nine radiologists (r=0.69±0.07). The correlation coefficient was improved compared to that of a distance-based method (r=0.58). The result indicated that similar images selected by the psychophysical similarity measure may be useful to radiologists in the diagnosis of clustered microcalcifications on mammograms. [ABSTRACT FROM AUTHOR]

The presentation of images that are similar to that of an unknown lesion seen on a mammogram may be helpful for radiologists to correctly diagnose that lesion. For similar images to be useful, they must be quite similar from the radiologists’ point of view. We have been trying to quantify the radiologists’ impression of similarity for pairs of lesions and to establish a “gold standard” for development and evaluation of a computerized scheme for selecting such similar images. However, it is considered difficult to reliably and accurately determine similarity ratings, because they are subjective. In this study, we compared the subjective similarities obtained by two different methods, an absolute rating method and a 2-alternative forced-choice (2AFC) method, to demonstrate that reliable similarity ratings can be determined by the responses of a group of radiologists. The absolute similarity ratings were previously obtained for pairs of masses and pairs of microcalcifications from five and nine radiologists, respectively. In this study, similarity ranking scores for eight pairs of masses and eight pairs of microcalcifications were determined by use of the 2AFC method. In the first session, the eight pairs of masses and eight pairs of microcalcifications were grouped and compared separately for determining the similarity ranking scores. In the second session, another similarity ranking score was determined by use of mixed pairs, i.e., by comparison of the similarity of a mass pair with that of a calcification pair. Four pairs of masses and four pairs of microcalcifications were grouped together to create two sets of eight pairs. The average absolute similarity ratings and the average similarity ranking scores showed very good correlations in the first study (Pearson’s correlation coefficients: 0.94 and 0.98 for masses and microcalcifications, respectively). Moreover, in the second study, the correlations between the absolute ratings and the ranking scores were also very high (0.92 and 0.96), which implies that the observers were able to compare the similarity of a mass pair with that of a calcification pair consistently. These results provide evidence that the concept of similarity for pairs of images is robust, even across different lesion types, and that radiologists are able to reliably determine subjective similarity for pairs of breast lesions. [ABSTRACT FROM AUTHOR]

The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification of maxillary sinus lesions compared with healthy maxillary sinuses. The imaging data for healthy maxillary sinuses (587 sinuses, Class 0), inflamed maxillary sinuses (416 sinuses, Class 1), cysts of maxillary sinus regions (171 sinuses, Class 2) were assigned to training, testing 1, and testing 2 data sets. A learning process of 1000 epochs with the training images and labels was performed using DetectNet, and a learning model was created. The testing 1 and testing 2 images were applied to the model, and the detection sensitivities and the false-positive rates per image were calculated. The accuracies, sensitivities and specificities were determined for distinguishing the inflammation group (Class 1) and cyst group (Class 2) with respect to the healthy group (Class 0). Detection sensitivities of healthy (Class 0) and inflamed (Class 1) maxillary sinuses were 100% for both testing 1 and testing 2 data sets, whereas they were 98 and 89% for cysts of the maxillary sinus regions (Class 2). False-positive rates per image were nearly 0.00. Accuracies, sensitivities and specificities for diagnosis maxillary sinusitis were 90–91%, 88–85%, and 91–96%, respectively; for cysts of the maxillary sinus regions, these values were 97–100%, 80–100%, and 100–100%, respectively. Deep learning could reliably detect the maxillary sinuses and identify maxillary sinusitis and cysts of the maxillary sinus regions. This study using a deep leaning object detection technique indicated that the detection sensitivities of maxillary sinuses were high and the performance of maxillary sinus lesion identification was ≧80%. In particular, performance of sinusitis identification was ≧90%. [ABSTRACT FROM AUTHOR]

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Purpose: The purpose of this study was to develop and evaluate lung cancer segmentation with a pretrained model and transfer learning. The pretrained model was constructed from an artificial dataset generated using a generative adversarial network (GAN). Materials and Methods: Three public datasets containing images of lung nodules/lung cancers were used: LUNA16 dataset, Decathlon lung dataset, and NSCLC radiogenomics. The LUNA16 dataset was used to generate an artificial dataset for lung cancer segmentation with the help of the GAN and 3D graph cut. Pretrained models were then constructed from the artificial dataset. Subsequently, the main segmentation model was constructed from the pretrained models and the Decathlon lung dataset. Finally, the NSCLC radiogenomics dataset was used to evaluate the main segmentation model. The Dice similarity coefficient (DSC) was used as a metric to evaluate the segmentation performance. Results: The mean DSC for the NSCLC radiogenomics dataset improved overall when using the pretrained models. At maximum, the mean DSC was 0.09 higher with the pretrained model than that without it. Conclusion: The proposed method comprising an artificial dataset and a pretrained model can improve lung cancer segmentation as confirmed in terms of the DSC metric. Moreover, the construction of the artificial dataset for the segmentation using the GAN and 3D graph cut was found to be feasible., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Nishio, Fujimoto, Matsuo, Muramatsu, Sakamoto and Fujita.)

Objective: The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification of maxillary sinus lesions compared with healthy maxillary sinuses., Methods: The imaging data for healthy maxillary sinuses (587 sinuses, Class 0), inflamed maxillary sinuses (416 sinuses, Class 1), cysts of maxillary sinus regions (171 sinuses, Class 2) were assigned to training, testing 1, and testing 2 data sets. A learning process of 1000 epochs with the training images and labels was performed using DetectNet, and a learning model was created. The testing 1 and testing 2 images were applied to the model, and the detection sensitivities and the false-positive rates per image were calculated. The accuracies, sensitivities and specificities were determined for distinguishing the inflammation group (Class 1) and cyst group (Class 2) with respect to the healthy group (Class 0)., Results: Detection sensitivities of healthy (Class 0) and inflamed (Class 1) maxillary sinuses were 100% for both testing 1 and testing 2 data sets, whereas they were 98 and 89% for cysts of the maxillary sinus regions (Class 2). False-positive rates per image were nearly 0.00. Accuracies, sensitivities and specificities for diagnosis maxillary sinusitis were 90-91%, 88-85%, and 91-96%, respectively; for cysts of the maxillary sinus regions, these values were 97-100%, 80-100%, and 100-100%, respectively., Conclusion: Deep learning could reliably detect the maxillary sinuses and identify maxillary sinusitis and cysts of the maxillary sinus regions., Advances in Knowledge: This study using a deep leaning object detection technique indicated that the detection sensitivities of maxillary sinuses were high and the performance of maxillary sinus lesion identification was ≧80%. In particular, performance of sinusitis identification was ≧90%.

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. Retinal fundus photography is frequently obtained for various eye disease diagnosis and record and is a suitable screening exam for its simplicity and low cost. However, the number of ophthalmologists who are specialized in glaucoma diagnosis is limited. We have been studying automated schemes for detection of nerve fiber layer defects and analysis of optic disc deformation, two major signs of glaucoma, in assisting ophthalmologists' accurate and efficient diagnosis. In this chapter, our recent progress in computerized methods is discussed.

Rationale: Multilineage-differentiating stress enduring (Muse) cells, pluripotent marker stage-specific embryonic antigen-3 + cells, are nontumorigenic endogenous pluripotent-like stem cells obtainable from various tissues including the bone marrow. Their therapeutic efficiency has not been validated in acute myocardial infarction., Objective: The main objective of this study is to clarify the efficiency of intravenously infused rabbit autograft, allograft, and xenograft (human) bone marrow-Muse cells in a rabbit acute myocardial infarction model and their mechanisms of tissue repair., Methods and Results: In vivo dynamics of Nano-lantern-labeled Muse cells showed preferential homing of the cells to the postinfarct heart at 3 days and 2 weeks, with ≈14.5% of injected GFP (green fluorescent protein)-Muse cells estimated to be engrafted into the heart at 3 days. The migration and homing of the Muse cells was confirmed pharmacologically (S1PR2 [sphingosine monophosphate receptor 2]-specific antagonist JTE-013 coinjection) and genetically (S1PR2-siRNA [small interfering ribonucleic acid]-introduced Muse cells) to be mediated through the S1P (sphingosine monophosphate)-S1PR2 axis. They spontaneously differentiated into cells positive for cardiac markers, such as cardiac troponin-I, sarcomeric α-actinin, and connexin-43, and vascular markers. GCaMP3 (GFP-based Ca calmodulin probe)-labeled Muse cells that engrafted into the ischemic region exhibited increased GCaMP3 fluorescence during systole and decreased fluorescence during diastole. Infarct size was reduced by ≈52%, and the ejection fraction was increased by ≈38% compared with vehicle injection at 2 months, ≈2.5 and ≈2.1 times higher, respectively, than that induced by mesenchymal stem cells. These effects were partially attenuated by the administration of GATA4 -gene-silenced Muse cells. Muse cell allografts and xenografts efficiently engrafted and recovered functions, and allografts remained in the tissue and sustained functional recovery for up to 6 months without immunosuppression., Conclusions: Muse cells may provide reparative effects and robust functional recovery and may, thus, provide a novel strategy for the treatment of acute myocardial infarction., (© 2018 American Heart Association, Inc.)

The pattern of blood vessels in the eye is unique to each person because it rarely changes over time. Therefore, it is well known that retinal blood vessels are useful for biometrics. This paper describes a biometrics method using the Jaccard similarity coefficient (JSC) based on blood vessel regions in retinal image pairs. The retinal image pairs were rough matched by the center of their optic discs. Moreover, the image pairs were aligned using the Iterative Closest Point algorithm based on detailed blood vessel skeletons. For registration, perspective transform was applied to the retinal images. Finally, the pairs were classified as either correct or incorrect using the JSC of the blood vessel region in the image pairs. The proposed method was applied to temporal retinal images, which were obtained in 2009 (695 images) and 2013 (87 images). The 87 images acquired in 2013 were all from persons already examined in 2009. The accuracy of the proposed method reached 100%.

Retinal arteriolar narrowing is decided based on the artery and vein diameter ratio (AVR). Previous methods segmented blood vessels and classified arteries and veins by color pixels in the centerlines of blood vessels. AVR was definitively determined through measurement of artery and vein diameters. However, this approach was not sufficient for cases with close contact between the artery of interest and an imposing vein. Here, an algorithm for AVR measurement via new classification of arteries and veins is proposed. In this algorithm, additional steps for an accurate segmentation of arteries and veins, which were not identified using the previous method, have been added to better identify major veins in the red channel of a color image. To identify major arteries, a decision tree with three features was used. As a result, all major veins and 90.9% of major arteries were correctly identified, and the absolute mean error in AVRs was 0.12. The proposed method will require further testing with a greater number of images of arteriolar narrowing before clinical application.

Detection of lacunar infarcts is important because their presence indicates an increased risk of severe cerebral infarction and dementia. However, accurate identification of lacunar infarcts is often difficult for radiologists. Our previous computer-aided detection (CAD) scheme achieved a sensitivity of 96.8% with 0.76 false positives (FPs) per slice. However, further reduction of FPs remained an issue for the clinical application. The purpose of this study is to improve our CAD scheme by using kernel eigenspace template matching. First, we selected the regions of interest (ROIs) around the candidate regions detected in our previous method. A kernel eigenspace was then made by using kernel principal component analysis of the training data set. A test ROI was projected onto the same kernel eigenspace as the training data set. The cross-correlation coefficients between the test ROI and all the training ROIs were calculated on the kernel eigenspace. By comparing the two maxima of coefficients with a lacunar ROI and an FP ROI, the test ROI was classified. By using the proposed method, the quantity of the templates became 1.9% of that in template matching on the real space and 31. 9% of FPs could be eliminated while keeping the same sensitivity; nevertheless 30.3% of FPs were eliminated when we employed the eigenspace template matching under the same condition. Therefore, kernel eigenspace template matching could improve FP rate without a significant reduction in the true positive rate.

The purpose of this study was to improve an automated scheme for detecting carotid artery calcification (CAC) in dental panoramic radiographs (DPRs). Using 100 DPRs, the sensitivity of CAC detection employing our previous method was 90.0% with 5.0 false positives (FPs) per image. This study describes two enhancements. One is the adoption of a new feature for the position of CACs in addition to previous features. The other is feature selection employing the support vector machine using all combinations. Five of 12 features were selected. Using our proposed method, the average sensitivity for the same database proved to be 90.0%, with only 2.5 FPs per image. These results indicate the potential effectiveness of the new positional feature and feature selection.

Glaucoma is a leading cause of permanent blindness. Retinal imaging is useful for early detection of glaucoma. In order to evaluate the presence of glaucoma, ophthalmologists may determine the cup and disc areas and diagnose glaucoma using a vertical optic cup-to-disc (C/D) ratio and a rim-to-disc (R/D) ratio. Previously we proposed a method to determine cup edge by analyzing a vertical profile of pixel values, but this method provided a cup edge smaller than that of an ophthalmologist. This paper describes an improved method using the locations of the blood vessel bends. The blood vessels were detected by a concentration feature determined from the density gradient. The blood vessel bends were detected by tracking the blood vessels from the disc edge to the primary cup edge, which was determined by our previous method. Lastly, the vertical C/D ratio and the R/D ratio were calculated. Using forty-four images, including 32 glaucoma images, the AUCs of both the vertical C/D ratio and R/D ratio by this proposed method were 0.966 and 0.936, respectively.

Diabetic retinopathy (DR) is the most frequent cause of blindness. Microaneurysm (MA) is an early symptom of DR. Therefore, the detection of MA is important for the early detection of DR. We have proposed an automated MA detection method based on double-ring filter, but it has given many false positives. In this paper, we propose an MA detection method based on eigenvalue analysis using a Hessian matrix, with an aim to improve MA detection. After image preprocessing, the MA candidate regions were detected by eigenvalue analysis using the Hessian matrix in green-channeled retinal fundus images. Then, 126 features were calculated for each candidate region. By a threshold operation based on feature analysis, false positive candidates were removed. The candidate regions were then classified either as MA or false positive using artificial neural networks (ANN) based on principal component analysis (PCA). The 126 features were reduced to 25 components by PCA, and were then inputted to ANN. When the method was evaluated on visible MAs using 25 retinal images from the retinopathy online challenge (ROC) database, the true positive rate was 73%, with eight false positives per image.