Assessing knee OA severity with CNN attention-based end-to-end architectures

This work proposes a novel end-to-end convolutional neural network (CNN) architecture to automatically quantify the severity of knee osteoarthritis (OA) using X-Ray images, which incorporates trainable attention modules acting as unsupervised fine-grained detectors of the region of interest (ROI). The proposed attention modules can be applied at different levels and scales across any CNN pipeline helping the network to learn relevant attention patterns over the most informative parts of the image at different resolutions. We test the proposed attention mechanism on existing state-of-the-art CNN architectures as our base models, achieving promising results on the benchmark knee OA datasets from the osteoarthritis initiative (OAI) and multicenter osteoarthritis study (MOST).Postprint (published version

Doctor Imitator: Hand-Radiography-based Bone Age Assessment by Imitating Scoring Methods

Bone age assessment is challenging in clinical practice due to the complicated bone age assessment process. Current automatic bone age assessment methods were designed with rare consideration of the diagnostic logistics and thus may yield certain uninterpretable hidden states and outputs. Consequently, doctors can find it hard to cooperate with such models harmoniously because it is difficult to check the correctness of the model predictions. In this work, we propose a new graph-based deep learning framework for bone age assessment with hand radiographs, called Doctor Imitator (DI). The architecture of DI is designed to learn the diagnostic logistics of doctors using the scoring methods (e.g., the Tanner-Whitehouse method) for bone age assessment. Specifically, the convolutions of DI capture the local features of the anatomical regions of interest (ROIs) on hand radiographs and predict the ROI scores by our proposed Anatomy-based Group Convolution, summing up for bone age prediction. Besides, we develop a novel Dual Graph-based Attention module to compute patient-specific attention for ROI features and context attention for ROI scores. As far as we know, DI is the first automatic bone age assessment framework following the scoring methods without fully supervised hand radiographs. Experiments on hand radiographs with only bone age supervision verify that DI can achieve excellent performance with sparse parameters and provide more interpretability.Comment: Original Title: "Doctor Imitator: A Graph-based Bone Age Assessment Framework Using Hand Radiographs" @inproceedings{chen2020doctor, title={Doctor imitator: A graph-based bone age assessment framework using hand radiographs}, author={Chen, Jintai and Yu, Bohan and Lei, Biwen and Feng, Ruiwei and Chen, Danny Z and Wu, Jian}, booktitle={MICCAI}, year={2020}

A Multi-featured Approach by Integrating Digital Hand and Dental X-Ray for Human Age Estimation

Traditionally, human bone age is estimated manually by inspecting the multiple body part X-ray images, which is extremely time-consuming and prone to error. The accuracy of the human estimate depends on the experience of the medical practitioner, and thus it suffers from intra- and inter-observer variability. Hence, efficient automatic approaches are required to determine human age with high accuracy. In this work, we propose a human age estimation technique using Deep Learning (DL) technique based on hand X-ray images combined with dental orthopantomographs (OPGs) is proposed. Here, the input X-ray image is pre-processed first using Non-Local Means (NLM) first, followed by Region of Interest (RoI) extraction. Later, color and position image augmentation are performed in order to balance the dataset. Thereafter, the salient features in the image are determined, and based on these features, human age estimation is carried out using the Deep Residual Network (DRN). Here, the DRN is trained using the Beluga whale lion optimization (BWLO) algorithm. Furthermore, the BWLO_DRN is examined for its superiority considering the model accuracy and is found to obtain value of 90.1% on hand-wrist and 89.9% OPG real time dataset, thus showing superior performance for hand-wrist images

Comparison among bone age assessment methods and development of a Fishman-based skeletal maturity determination system using deep learning in contemporary Korean children and adolescents

학위논문 (박사) -- 서울대학교 대학원 : 치의학대학원 치의과학과, 2020. 8. 허민석.Purpose Greulich-Pyle (GP), Tanner-Whitehouse 3 (TW3), and Fishman methods are typically employed for bone age assessment from hand-wrist radiographs. This study aimed to compare the Fishman method with the GP and TW3 methods and investigate the reliability of Fishmans skeletal maturity indicators (SMIs) for contemporary healthy Korean children and adolescents, and to develop a new fully-automated SMI-based skeletal maturity determination system using deep neural networks and evaluate the accuracy of the system. Materials and Methods The left hand-wrist radiographs of 1,617 subjects (706 males and 911 females; 6–17 years of age) taken in 2012–2017 were selected. Bone ages were calculated using the GP, TW3, and Fishman methods, and compared with chronological ages using paired t-test and correlation analysis. For developing a fully-automated deep learning system for skeletal maturity determination using the Fishman method, two skeletal maturity determination systems were developed and their accuracies were compared. A system was trained with an SMI-labeled dataset, and another one was trained with a dataset that was not only labeled with SMIs but was additionally labeled considering the region of interest (ROI) extraction and skeletal maturity determination for each ROI. Two oral and maxillofacial radiologists established a reference standard for the SMIs. Results The bone ages significantly differed with the chronological ages in the whole group and gender subgroups for all three methods except in the male group for the TW3 method. However, a high degree of correlation was observed between the chronological ages and the bone ages when evaluated by all the methods. For the skeletal maturity determination system that was trained using the dataset labeled with only SMIs, the mean absolute error (MAE) was 0.88 and the within-1 concordance rate was 73.1 %. Conversely, the system consisting of ROI extraction, ROI-based skeletal maturity determination, and final SMI prediction showed much better outcomes; the MAE was 0.34 and the within-1 concordance rate was 93.7 %. Conclusions In this study, Fishmans SMI was confirmed as a reliable index for the determination of skeletal maturity from hand-wrist radiographs. A developed deep learning system automated the entire process consisting of ROI extraction, skeletal maturity determination for each ROI, and final SMI prediction. The systems accuracy in predicting skeletal maturity was outstanding. Thus, the system presented in this study can be applied effectively to determine the skeletal maturity of contemporary Korean children and adolescents.목적 수완부방사선영상을 이용한 골령 평가에는 Greulich-Pyle(GP)법, Tanner-Whitehous 3(TW3)법 및 Fishman법이 주로 이용되고 있다. 본 연구에서는 건강한 현대 한국 소아 및 청소년의 수완부방사선영상을 이용해 위의 3가지 골령 평가 방법을 비교하여 Fishman의 골격성숙도지수(skeletal maturity indicator; SMI)의 유용성을 검증하고, SMI를 기반으로 하여 심층 신경망을 이용한 골격 성숙도 측정 딥러닝 시스템을 개발한 후 시스템의 정확도를 평가하고자 하였다. 재료 및 방법 본 연구를 위해 2012-2017년에 촬영된 좌측 수완부방사선영상 1,617매(남: 706, 여: 911; 연령 6-17세)를 수집하였다. GP법, TW3법 및 Fishman법에 따라 측정한 골령과 연대기적 연령의 관계를 대응 표본 t 검정 및 상관관계 분석을 이용하여 비교 평가하였다. 또한 Fishman법에 기반하여 골격 성숙도를 측정하는 완전 자동화 딥러닝 시스템을 개발하고자, SMI만으로 라벨링한 데이터를 이용해 학습을 시행한 시스템과 관심 부위, 관심 부위 별 골격 성숙도 및 SMI로 라벨링한 데이터를 이용해 학습을 시행한 시스템을 각각 개발하여 이들의 정확도를 평가하였다. SMI에 대한 참조 표준은 영상치의학 판독의 2인이 토의하여 작성하였다. 결과 TW3법의 남성 그룹을 제외하고는 3가지 방법에 있어 전체 그룹 및 성별 하위그룹에서 골령과 연대기적 연령 사이에 유의한 차이가 있었다. 그러나 3가지 방법에서 모두 골령과 연대기적 연령 사이에 높은 상관관계가 관찰되었다. SMI만으로 라벨링한 데이터를 이용해 학습을 시행한 시스템의 경우, SMI의 평균 절대 오차 값이 0.88, 1등급 내 일치도는 73.1 %였다. 관심 부위 추출, 관심 부위 별 성숙도 평가 및 최종 SMI 평가로 구성된 시스템의 경우, 보다 우수한 결과를 나타내어 그 값이 각각 0.34, 93.7 %였다. 결론 본 연구에서 Fishman의 SMI는 수완부방사선영상을 이용한 골격 성숙도 측정에 있어 신뢰할 만한 척도로 평가되었다. 또한 본 연구에서 관심 부위 추출, 관심 부위 별 골격 성숙도 측정 및 최종 SMI 예측으로 구성되는 전 과정을 자동화한 딥러닝 시스템이 개발되었으며, 이는 골격 성숙도 측정에 있어 우수한 정확도를 보여주었다. 따라서 개발된 딥러닝 시스템은 현대 한국 아동 및 청소년의 골격 성숙도 측정에 유효하게 사용될 수 있을 것이다.I. Introduction 1 II. Materials and Methods 8 III. Results 21 IV. Discussion 33 V. Conclusion 38 VI. References 39 Abstract(Korean) 43Docto

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 190, February 1979

This bibliography lists 235 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1979

MedGAN: Medical Image Translation using GANs

Image-to-image translation is considered a new frontier in the field of medical image analysis, with numerous potential applications. However, a large portion of recent approaches offers individualized solutions based on specialized task-specific architectures or require refinement through non-end-to-end training. In this paper, we propose a new framework, named MedGAN, for medical image-to-image translation which operates on the image level in an end-to-end manner. MedGAN builds upon recent advances in the field of generative adversarial networks (GANs) by merging the adversarial framework with a new combination of non-adversarial losses. We utilize a discriminator network as a trainable feature extractor which penalizes the discrepancy between the translated medical images and the desired modalities. Moreover, style-transfer losses are utilized to match the textures and fine-structures of the desired target images to the translated images. Additionally, we present a new generator architecture, titled CasNet, which enhances the sharpness of the translated medical outputs through progressive refinement via encoder-decoder pairs. Without any application-specific modifications, we apply MedGAN on three different tasks: PET-CT translation, correction of MR motion artefacts and PET image denoising. Perceptual analysis by radiologists and quantitative evaluations illustrate that the MedGAN outperforms other existing translation approaches.Comment: 16 pages, 8 figure

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