Medical image denoising using convolutional denoising autoencoders

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.Comment: To appear: 6 pages, paper to be published at the Fourth Workshop on Data Mining in Biomedical Informatics and Healthcare at ICDM, 201

Detection of Proximal Caries at The Molar Teeth Using Edge Enhancement Algorithm

Panoramic X-Ray produces produces the most common oral digital radiographic image that it used in dentistry practice. The image can further improve accuracy compared to analog one. This study aims to establish proximal caries edge on enhancement images so they can be easily recognized. The images were obtained from the Department of Radiology, General Hospital of M. Djamil Padang Indonesia. Total file of images to be tested were 101. Firstly, the images are analyzed by dentists who practiced at Segment Padang Hospital Indonesia. They concluded that there is proximal caries in 30 molar teeth. Furthermore, the images were processed using Matlab software with the following steps, i.e. cropping, enhancement, edge detection, and edge enhancement. The accuracy rate of detection of edge enhancement images being compared with that of dentist analysis was 73.3%. In the edge enhancement images proximal caries edge can be found conclusively in 22 teeth and dubiously in eight teeth. The results of this study convinced that edge enhancement images can be recommended to assist dentists in detecting proximal caries.

Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs

Periapical radiolucencies, which can be detected on panoramic radiographs, are one of the most common radiographic findings in dentistry and have a differential diagnosis including infections, granuloma, cysts and tumors. In this study, we seek to investigate the ability with which 24 oral and maxillofacial (OMF) surgeons assess the presence of periapical lucencies on panoramic radiographs, and we compare these findings to the performance of a predictive deep learning algorithm that we have developed using a curated data set of 2902 de-identified panoramic radiographs. The mean diagnostic positive predictive value (PPV) of OMF surgeons based on their assessment of panoramic radiographic images was 0.69(± 0.13), indicating that dentists on average falsely diagnose 31% of cases as radiolucencies. However, the mean diagnostic true positive rate (TPR) was 0.51(± 0.14), indicating that on average 49% of all radiolucencies were missed. We demonstrate that the deep learning algorithm achieves a better performance than 14 of 24 OMF surgeons within the cohort, exhibiting an average precision of 0.60(± 0.04), and an F1 score of 0.58(± 0.04) corresponding to a PPV of 0.67(± 0.05) and TPR of 0.51(± 0.05). The algorithm, trained on limited data and evaluated on clinically validated ground truth, has potential to assist OMF surgeons in detecting periapical lucencies on panoramic radiographs

'Aariz: A Benchmark Dataset for Automatic Cephalometric Landmark Detection and CVM Stage Classification

The accurate identification and precise localization of cephalometric landmarks enable the classification and quantification of anatomical abnormalities. The traditional way of marking cephalometric landmarks on lateral cephalograms is a monotonous and time-consuming job. Endeavours to develop automated landmark detection systems have persistently been made, however, they are inadequate for orthodontic applications due to unavailability of a reliable dataset. We proposed a new state-of-the-art dataset to facilitate the development of robust AI solutions for quantitative morphometric analysis. The dataset includes 1000 lateral cephalometric radiographs (LCRs) obtained from 7 different radiographic imaging devices with varying resolutions, making it the most diverse and comprehensive cephalometric dataset to date. The clinical experts of our team meticulously annotated each radiograph with 29 cephalometric landmarks, including the most significant soft tissue landmarks ever marked in any publicly available dataset. Additionally, our experts also labelled the cervical vertebral maturation (CVM) stage of the patient in a radiograph, making this dataset the first standard resource for CVM classification. We believe that this dataset will be instrumental in the development of reliable automated landmark detection frameworks for use in orthodontics and beyond

Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray

Dental caries is one of the most chronic diseases that involves the majority of people at least once during their lifetime. This expensive disease accounts for 5-10% of the healthcare budget in developing countries. Caries lesions appear as the result of dental biofi lm metabolic activity, caused by bacteria (most prominently Streptococcus mutans) feeding on uncleaned sugars and starches in oral cavity. Also known as tooth decay, they are primarily diagnosed by general dentists solely based on clinical assessments. Since in many cases dental problems cannot be detected with simple observations, dental x-ray imaging is introduced as a standard tool for domain experts, i.e. dentists and radiologists, to distinguish dental diseases, such as proximal caries. Among different dental radiography methods, Panoramic or Orthopantomogram (OPG) images are commonly performed as the initial step toward assessment. OPG images are captured with a small dose of radiation and can depict the entire patient dentition in a single image. Dental caries can sometimes be hard to identify by general dentists relying only on their visual inspection using dental radiography. Tooth decays can easily be misinterpreted as shadows due to various reasons, such as low image quality. Besides, OPG images have poor quality and structures are not presented with strong edges due to low contrast, uneven exposure, etc. Thus, disease detection is a very challenging task using Panoramic radiography. With the recent development of Artificial Intelligence (AI) in dentistry, and with the introduction of Convolutional Neural Network (CNN) for image classification, developing medical decision support systems is becoming a topic of interest in both academia and industry. Providing more accurate decision support systems using CNNs to assist dentists can enhance their diagnosis performance, resulting in providing improved dental care assistance for patients. In the following thesis, the first automated teeth extraction system for Panoramic images, using evolutionary algorithms, is proposed. In contrast to other intraoral radiography methods, Panoramic is captured with x-ray film outside the patient mouth. Therefore, Panoramic x-rays contain regions outside of the jaw, which make teeth segmentation extremely difficult. Considering that we solely need an image of each tooth separately to build a caries detection model, segmentation of teeth from the OPG image is essential. Due to the absence of significant pixel intensity difference between different regions in OPG radiography, teeth segmentation becomes very hard to implement. Consequently, an automated system is introduced to get an OPG as input and gives images of single teeth as the output. Since only a few research studies are utilizing similar task for Panoramic radiography, there is room for improvement. A genetic algorithm is applied along with different image processing methods to perform teeth extraction by jaw extraction, jaw separation, and teeth-gap valley detection, respectively. The proposed system is compared to the state-of-the-art in teeth extraction on other image types. After teeth are segmented from each image, a model based on various untrained and pretrained CNN-based architectures is proposed to detect dental caries for each tooth. Autoencoder-based model along with famous CNN architectures are used for feature extraction, followed by capsule networks to perform classification. The dataset of Panoramic x-rays is prepared by the authors, with help from an expert radiologist to provide labels. The proposed model has demonstrated an acceptable detection rate of 86.05%, and an increase in caries detection speed. Considering the challenges of performing such task on low quality OPG images, this work is a step towards developing a fully automated efficient caries detection model to assist domain experts