Deep learning for automated skeletal bone age assessment in X-ray images

Spampinato, Concetto; Palazzo, Simone; Giordano, Daniela; Aldinucci, Marco; Leonardi, Rosalia

doi:10.1016/j.media.2016.10.010

Cited by 359 publications

(255 citation statements)

References 43 publications

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“…Prostate segmentation in MRI Liao et al (2013) Application of stacked independent subspace analysis networks Cheng et al (2016b) CNN produces energy map for 2D slice based active appearance segmentation Guo et al (2016) Stacked sparse auto-encoders extract features from patches, input to atlas matching and a deformable model Milletari et al (2016b) 3D U-net based CNN architecture with objective function that directly optimizes Dice coefficient, ranks #5 in PROMISE12 Yu et al (2017) 3D fully convolutional network, hybrid between a ResNet and U-net architecture, ranks #1 on PROMISE12 Chen et al (2015c) CT Vertebrae localization; joint learning of vertebrae appearance and dependency on neighbors using CNN Roth et al (2015c) CT Sclerotic metastases detection; random 2D views are analyzed by CNN and aggregated Shen et al (2015a) CT Vertebrae localization and segmentation; CNN for segmenting vertebrae and for center detection Suzani et al (2015) MRI Vertebrae localization, identification and segmentation of vertebrae; CNN used for initial localization Yang et al (2015) MRI Anatomical landmark detection; uses CNN for slice classification for presence of landmark Antony et al (2016) X-ray Osteoarthritis grading; pre-trained ImageNet CNN fine-tuned on knee X-rays Cai et al (2016b) CT, MRI Vertebrae localization; RBM determines position, orientation and label of vertebrae Golan et al (2016) US Hip dysplasia detection; CNN with adversarial component detects structures and performs measurements Korez et al (2016) MRI Vertebral bodies segmentation; voxel probabilities obtained with a 3D CNN are input to deformable model Jamaludin et al (2016) MRI Automatic spine scoring; VGG-19 CNN analyzes vertebral discs and finds lesion hotspots Miao et al (2016) X-ray Total Knee Arthroplasty kinematics by real-time 2D/3D registration using CNN Roth et al (2016c) CT Posterior-element fractures detection; CNN for 2.5D patch-based analysiš Stern et al (2016) MRI Hand age estimation; 2D regression CNN analyzes 13 bones Forsberg et al (2017) MRI Vertebrae detection and labeling; outputs of two CNNs are input to graphical model Spampinato et al (2017) X-ray Skeletal bone age assessment; comparison among several deep learning approaches for the task at hand A surprising number of complete applications with promising results are available; one that stands out is Jamaludin et al (2016) who trained their system with 12K discs and claimed near-human performances across four different radiological scoring ta...…”

Section: Musculoskeletalmentioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

10,074

5,414

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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. Concise overviews are provided of studies per application area: neuro, retinal, pulmonary, digital pathology, breast, cardiac, abdominal, musculoskeletal. We end with a summary of the current state-of-the-art, a critical discussion of open challenges and directions for future research.

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Section: Musculoskeletalmentioning

confidence: 99%

A survey on deep learning in medical image analysis

Litjens

Kooi

Bejnordi

et al. 2017

Medical Image Analysis

10,074

5,414

View full text Add to dashboard Cite

show abstract

“…Using deep learning, [26] constructs a regression network, called BoNet, based on the OverFeat model. Similarly, [27] employ several pre-trained and finetuned networks, based on GoogleNet, AlexNet and VGG-16.…”

Section: Discussionmentioning

confidence: 99%

Ossification area localization in pediatric hand radiographs using deep neural networks for object detection

et al. 2018

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BackgroundDetection of ossification areas of hand bones in X-ray images is an important task, e.g. as a preprocessing step in automated bone age estimation. Deep neural networks have emerged recently as de facto standard detection methods, but their drawback is the need of large annotated datasets. Finetuning pre-trained networks is a viable alternative, but it is not clear a priori if training with small annotated datasets will be successful, as it depends on the problem at hand. In this paper, we show that pre-trained networks can be utilized to produce an effective detector of ossification areas in pediatric X-ray images of hands.Methods and findingsA publicly available Faster R-CNN network, pre-trained on the COCO dataset, was utilized and finetuned with 240 manually annotated radiographs from the RSNA Pediatric Bone Age Challenge, which comprises over 14.000 pediatric radiographs. The validation is done on another 89 radiographs from the dataset and the performance is measured by Intersection-over-Union (IoU). To understand the effect of the data size on the pre-trained network, subsampling was applied to the training data and the training was repeated. Additionally, the network was trained from scratch without any pre-trained weights. Finally, to understand whether the trained model could be useful, we compared the inference of the network to an annotation of an expert radiologist. The finetuned network was able to achieve an average precision (mAP@0.5IoU) of 92.92 ± 1.93. Apart from the wrist region, all ossification areas were able to benefit from more data. In contrast, the network trained from scratch was not able to produce any correct results. When compared to the annotations of the expert radiologist, the network was able to localize the regions quite well, as the F1-Score was on average 91.85 ± 1.06.ConclusionsBy finetuning a pre-trained deep neural network, with 240 annotated radiographs, we were able to successfully detect ossification areas in prediatric hand radiographs.

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“…There were many advanced CNN architectures such as AlexNet, GoogLeNet and residual network, which have been applied for bone age assessment with good performance. One straight choice was to apply such CNN architectures to the recognized sub‐regions.…”

Section: Methodsmentioning

confidence: 99%

Landmark‐based multi‐region ensemble convolutional neural networks for bone age assessment

Cao

Chen

et al. 2019

Int J Imaging Syst Tech

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Bone age assessment based on hand X‐ray imaging is important in pediatry medicine. At present, prediction of bone age is mainly performed by the manual comparison with the existing atlas. To develop an automatic regression framework based on deep learning with high performance and efficiency. A landmark‐based multi‐region convolutional neural networks for automatic bone age assessment based on left hand X‐ray images was proposed. The deep alignment network localized multiple landmarks distributed over the hand, and cropped the local regions to establish the multi‐region ensemble convolutional neural networks with different sub‐network combinations. The modified loss function and the optimized bone sub‐regions were applied to train the networks. The experiments on Digital Hand Atlas Database revealed that the mean absolute error of bone age assessment was 0.52 ± 0.25 years. It is the first study to predict bone age using deep learning methods throughout the entire process including image preprocessing, landmark localization and bone age predication. The proposed method outperformed most of the existing state‐of‐the‐art deep learning methods and achieved good results compared with the expert's experience. It can improve the efficiency of the medical doctors while minimizing the subjective errors.

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Deep learning for automated skeletal bone age assessment in X-ray images

Cited by 359 publications

References 43 publications

A survey on deep learning in medical image analysis

A survey on deep learning in medical image analysis

Ossification area localization in pediatric hand radiographs using deep neural networks for object detection

Landmark‐based multi‐region ensemble convolutional neural networks for bone age assessment

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