Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs.

Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient's panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.

Computer Aided Diagnosis System for Detection of Cancer Cells on Cytological Pleural Effusion Images.

Cytological screening plays a vital role in the diagnosis of cancer from the microscope slides of pleural effusion specimens. However, this manual screening method is subjective and time-intensive and it suffers from inter- and intra-observer variations. In this study, we propose a novel Computer Aided Diagnosis (CAD) system for the detection of cancer cells in cytological pleural effusion (CPE) images. Firstly, intensity adjustment and median filtering methods were applied to improve image quality. Cell nuclei were extracted through a hybrid segmentation method based on the fusion of Simple Linear Iterative Clustering (SLIC) superpixels and K-Means clustering. A series of morphological operations were utilized to correct segmented nuclei boundaries and eliminate any false findings. A combination of shape analysis and contour concavity analysis was carried out to detect and split any overlapped nuclei into individual ones. After the cell nuclei were accurately delineated, we extracted 14 morphometric features, 6 colorimetric features, and 181 texture features from each nucleus. The texture features were derived from a combination of color components based first order statistics, gray level cooccurrence matrix and gray level run-length matrix. A novel hybrid feature selection method based on simulated annealing combined with an artificial neural network (SA-ANN) was developed to select the most discriminant and biologically interpretable features. An ensemble classifier of bagged decision trees was utilized as the classification model for differentiating cells into either benign or malignant using the selected features. The experiment was carried out on 125 CPE images containing more than 10500 cells. The proposed method achieved sensitivity of 87.97%, specificity of 99.40%, accuracy of 98.70%, and F-score of 87.79%.

Comparative Study on Automated Cell Nuclei Segmentation Methods for Cytology Pleural Effusion Images.

Automated cell nuclei segmentation is the most crucial step toward the implementation of a computer-aided diagnosis system for cancer cells. Studies on the automated analysis of cytology pleural effusion images are few because of the lack of reliable cell nuclei segmentation methods. Therefore, this paper presents a comparative study of twelve nuclei segmentation methods for cytology pleural effusion images. Each method involves three main steps: preprocessing, segmentation, and postprocessing. The preprocessing and segmentation stages help enhancing the image quality and extracting the nuclei regions from the rest of the image, respectively. The postprocessing stage helps in refining the segmented nuclei and removing false findings. The segmentation methods are quantitatively evaluated for 35 cytology images of pleural effusion by computing five performance metrics. The evaluation results show that the segmentation performances of the Otsu, k-means, mean shift, Chan-Vese, and graph cut methods are 94, 94, 95, 94, and 93%, respectively, with high abnormal nuclei detection rates. The average computational times per image are 1.08, 36.62, 50.18, 330, and 44.03 seconds, respectively. The findings of this study will be useful for current and potential future studies on cytology images of pleural effusion.

Investigation of visually induced motion sickness in dynamic 3D contents based on subjective judgment, heart rate variability, and depth gaze behavior.

Visually induced motion sickness (VIMS) is an important safety issue in stereoscopic 3D technology. Accompanying subjective judgment of VIMS with objective measurement is useful to identify not only biomedical effects of dynamic 3D contents, but also provoking scenes that induce VIMS, duration of VIMS, and user behavior during VIMS. Heart rate variability and depth gaze behavior are appropriate physiological indicators for such objective observation. However, there is no information about relationship between subjective judgment of VIMS, heart rate variability, and depth gaze behavior. In this paper, we present a novel investigation of VIMS based on simulator sickness questionnaire (SSQ), electrocardiography (ECG), and 3D gaze tracking. Statistical analysis on SSQ data shows that nausea and disorientation symptoms increase as amount of dynamic motions increases (nausea: p<;0.005; disorientation: p<;0.05). To reduce VIMS, SSQ and ECG data suggest that user should perform voluntary gaze fixation at one point when experiencing vertical motion (up or down) and horizontal motion (turn left and right) in dynamic 3D contents. Observation of 3D gaze tracking data reveals that users who experienced VIMS tended to have unstable depth gaze than ones who did not experience VIMS.

Effect of phase difference in multi-antenna microwave thermal ablation for breast cancer treatment.

It was realized that cancer in breast is one of the most health hazards threatening women around the world for many years. Thermal ablation by using microwave energy is another alternative surgical maneuver due to its minimally invasive therapeutic technique. In this research, we investigate an effect of phase difference between three adjacent opened-slot coaxial probes in a multiple antenna alignment of microwave thermal ablation system for breast cancer treatment. FEM by using COMSOL is an implementation tools to simulate for 0, 45, 90, 135 and 180 degree of phase difference. 3D Simulation results show that temperature distribution pattern, destructive volume and SAR in breast tissue are affected from those phase-shift utilization in multi-antenna system significantly.

3D gaze tracking system for NVidia 3D Vision®.

Inappropriate parallax setting in stereoscopic content generally causes visual fatigue and visual discomfort. To optimize three dimensional (3D) effects in stereoscopic content by taking into account health issue, understanding how user gazes at 3D direction in virtual space is currently an important research topic. In this paper, we report the study of developing a novel 3D gaze tracking system for Nvidia 3D Vision(®) to be used in desktop stereoscopic display. We suggest an optimized geometric method to accurately measure the position of virtual 3D object. Our experimental result shows that the proposed system achieved better accuracy compared to conventional geometric method by average errors 0.83 cm, 0.87 cm, and 1.06 cm in X, Y, and Z dimensions, respectively.

A cost-sensitive extension of AdaBoost with markov random field priors for automated segmentation of breast tumors in ultrasonic images.

PURPOSE: A cost-sensitive extension of AdaBoost based on Markov random field (MRF) priors was developed to train an ensemble segmentation process which can avoid irregular shape, isolated points and holes, leading to lower error rate. The method was applied to breast tumor segmentation in ultrasonic images. METHODS: A cost function was introduced into the AdaBoost algorithm that penalizes dissimilar adjacent labels in MRF regularization. The extended AdaBoost algorithm generates a series of weak segmentation processes by sequentially selecting a process whose error rate weighted by the cost is minimum. The method was tested by generation of an ensemble segmentation process for breast tumors in ultrasonic images. This was followed by a active contour to refine the extracted tumor boundary. RESULTS: The segmentation performance was evaluated by tenfold cross validation test, where 300 carcinomas, 50 fibroadenomas, and 50 cysts were used. The experimental results revealed that the error rate of the proposed ensemble segmentation was two-thirds the error rate of the segmentation trained by AdaBoost without MRF. By combining the ensemble segmentation with a geodesic active contour, the average Jaccard index between the extracted tumors and the manually segmented true regions was 93.41%, significantly higher than the conventional segmentation process. CONCLUSION: A cost-sensitive extension of AdaBoost based on MRF priors provides an efficient and accurate means for the segmentation of tumors in breast ultrasound images.

Discrimination of breast tumors in ultrasonic images using an ensemble classifier based on the AdaBoost algorithm with feature selection.

This paper proposes a novel algorithm to estimate a log-compressed K distribution parameter and presents an algorithm to discriminate breast tumors in ultrasonic images. We computed a total of 208 features for discrimination, including those based on a parameter of a log-compressed K-distribution, which quantifies the homogeneity of the echo pattern in the tumor, but is influenced by compression parameters in the ultrasonic device. The proposed algorithm estimates the parameter of the log-compressed K-distribution in a manner free from this influence. To quantify irregularities in tumor shape, pattern-spectrum-based features were newly developed in this paper. The discrimination process uses an ensemble classifier trained by a multiclass AdaBoost learning algorithm (AdaBoost.M2), combined with a sequential feature-selection process. A 10-fold cross-validation test validated the performance, and the results were compared with those of a Mahalanobis distance-based classifier and a multiclass support vector machine. A total of 200 carcinomas, 50 fibroadenomas, and 50 cysts were used in the experiments. This paper demonstrates that the combination of a classifier trained by AdaBoost.M2 and features based on the estimated parameter of a log-compressed K-distribution, as well as those of the pattern spectrum, are useful for the discrimination of tumors.

Proposal of differential volume rendering reconstruction from X-ray fluoroscopy for clinical veterinary medicine.

3D reconstruction from ordinary X-ray equipment which is not CT or MRI is required in clinical veterinary medicine. In this paper, we propose a method for 3D-reconstruction from X-ray fluoroscopy for clinical veterinary medicine. Fluoroscopy is usually used to observe a movement of organ or to identify a position of organ for surgery by weak X-ray intensity. A problem arises due to weak X-ray intensity. Although fluoroscopy can present information of not only bone structure but soft tissues, the contrast is very low and it is very difficult to recognize some soft tissues. To solve this problem, this paper proposes a new method to determine opacity in volume rendering process. The opacity is determined according to 3D differential coefficient of 3D reconstruction. This differential volume rendering can present a 3D structure image of multiple organs volumetrically and clearly for clinical veterinary medicine. This paper shows results of experimental investigation of small dog.