Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from Pediatric Bone Age Challenge organized by RSNA 2017. The dataset for this competition is consisted of 12.6k radiological images of left hand labeled by the bone age and sex of patients. Our approach utilizes several deep learning architectures: U-Net, ResNet-50, and custom VGG-style neural networks trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and inference. This approach allows us to measure importance of specific hand bones for the automated bone age analysis. We further evaluate performance of the method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.Comment: 14 pages, 9 figure

Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation

Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning manner wherein each step the teacher module provides a refinement for the student network to learn with newly unlabeled data. Experimental results demonstrate the superiority of the proposed method over conventional supervised deep learning methods.Comment: IEEE BHI 2019 accepte

Fully Automated Bone Age Assessment On Large-Scale Hand X-Ray Dataset

Bone age assessment (BAA) is an essential topic in the clinical practice of evaluating the biological maturity of children. Because the manual method is time-consuming and prone to observer variability, it is attractive to develop computer-aided and automated methods for BAA. In this paper, we present a fully automatic BAA method. To eliminate noise in a raw X-ray image, we start with using U-Net to precisely segment hand mask image from a raw X-ray image. Even though U-Net can perform the segmentation with high precision, it needs a bigger annotated dataset. To alleviate the annotation burden, we propose to use deep active learning (AL) to select unlabeled data samples with sufficient information intentionally. These samples are given to Oracle for annotation. After that, they are then used for subsequential training. In the beginning, only 300 data are manually annotated and then the improved U-Net within the AL framework can robustly segment all the 12611 images in RSNA dataset. The AL segmentation model achieved a Dice score at 0.95 in the annotated testing set. To optimize the learning process, we employ six off-the-shell deep Convolutional Neural Networks (CNNs) with pretrained weights on ImageNet. We use them to extract features of preprocessed hand images with a transfer learning technique. In the end, a variety of ensemble regression algorithms are applied to perform BAA. Besides, we choose a specific CNN to extract features and explain why we select that CNN. Experimental results show that the proposed approach achieved discrepancy between manual and predicted bone age of about 6.96 and 7.35 months for male and female cohorts, respectively, on the RSNA dataset. These accuracies are comparable to state-of-the-art performance

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

Convolutional Neural Network Model for Sex Determination Using Femur Bones

Forensic anthropology is the critical discipline that applies physical anthropology in forensic education. One valuable application is the identification of the biological profile. However, in the aftermath of significant disasters, the identification of human skeletons becomes challenging due to their incompleteness and difficulty determining sex. Researchers have explored alternative indicators to address this issue, including using the femur bone as a reliable sex identifier. The development of artificial intelligence has created a new field called deep learning that has excelled in various applications, including sex determination using the femur bone. In this study, we employ the Convolutional Neural Network (CNN) method to identify the sex of human skeleton shards. A CNN model was trained on 91 CT-scan results of femur bones collected from Universiti Teknologi Malaysia, comprising 50 female and 41 male patients. The data pre-processing involves cropping, and the dataset is divided into training and validation subsets with varying percentages (60:4, 70:30, and 80:20). The constructed CNN architecture exhibits exceptional accuracy, achieving 100% accuracy in both training and validation data. Moreover, the precision, recall, and F1 score attained a perfect score of 1, validating the model's precise predictions. The results of this research demonstrate excellent accuracy, confirming the reliability of the developed model for sex determination. These findings demonstrate that using deep learning for sex determination is a novel and promising approach. The high accuracy of the CNN model provides a valuable tool for sex determination in challenging scenarios. This could have important implications for forensic investigations and help identify victims of disasters and other crimes

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

흉부 방사선 사진을 활용한 인공지능 기반 노령견 나이 추정에 관한 연구

학위논문(석사) -- 서울대학교대학원 : 수의과대학 수의학과, 2023. 2. 최지혜.Background: Age is a vital information that impacts all facets of veterinary medicine and accurate estimation of age is needed in fields such as emergency and shelter medicine. A few studies involving age estimation based on deep learning of medical images exists in human medicine, however, in dogs automatic age prediction with machine learning has been based solely on non-medical portrait images. Purpose: This study proposes a deep learning solution for age estimation in geriatric dogs from thoracic radiographs and sets the stage for future deep learning research involving other medical imaging modalities and for development of age-related biomarkers. Method: A large dataset of canine thoracic radiographs was utilized to train and test the convolutional neural networks performance in the estimation of age based on performance metric mean absolute error. Results: The network was able to extract age-related information from thoracic radiographs of geriatric dogs to estimate age with moderate correlation to the ground truth and through the analysis of activation maps, the vertebra was identified as the main region containing age-related information consistent with previously known sites of degenerative changes. Conclusion: The convolutional neural networks feasibility in geriatric age estimation using canine thoracic radiographs was confirmed by the results of this study and allowed visualization of areas that may contain age-related information deciphered by artificial intelligence.연령은 수의학의 모든 측면에 영향을 미치는 중요한 정보이며, 이 연구는 개 흉부 방사선 영상을 사용하여 노령견들의 연령 추정을 하는 deep learning solution을 제안합니다. 이 연구는 나이 추정을 위해 컨볼루션 신경망을 학습시키고 성능을 시험하기 위해 공개적으로 사용 가능한 대규모 방사선 데이터 세트와 동물 의료 기관의 추가 데이터를 사용하여 수행되었습니다. 이 네트워크는 흉부 방사선 영상에서 연령 관련 정보를 추출하여 상당히 정확하게 노령견에서 연령들을 추정할 수 있었습니다. 또한 활성화 맵 분석을 통해 축근골격계가 연령 관련 정보를 포함하는 주요 영역으로 식별되었으며 이 결과는 이전 연구들에서 알려졌던 축근골격계에서 나타나는 퇴행성 변화들을 네트워크가 인식했을 가능성을 제시합니다. 흉부 방사선 촬영은 여전히 호흡기 및 심혈관 질환을 선별하기 위해 수의학에서 가장 자주 사용되는 영상 검사 중 하나이기 때문에 동물보호소 의학, 예방 의학 및 반려 동물 보험과 같은 영역에서 자동 연령 추정의 실제 사용을 기대할 수 있습니다. 본연구는 딥 러닝과 의료 영상을 연령 추정에 활용해본 수의학 분야 최초의 연구로서, 향후 소동물의학 분야에서 컨볼루션 신경망을 활용한 연령 측정 및 연령 관련 바이오마커 개발 연구등의 발판을 마련하고자 합니다.Introduction 1 Materials and methods 5 1. Dataset 5 2. Hardware specification 8 3. Training, validation, and testing 9 4. Statistical correlation analysis 11 5. Activation map quantification 13 Results 14 Discussion 27 Conclusion 30 References 31 국문초록 40석