Automated anatomical landmark detection ondistal femur surface using convolutional neural network

Yang, Dong; Zhang, Shaoting; Yan, Zhennan; Tan, Chaoqun; Li, Kang; Metaxas, Dimitris N.

doi:10.1109/isbi.2015.7163806

Cited by 78 publications

(54 citation statements)

References 13 publications

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“…Significant differences in landmarking accuracy were found in the literature, depending on the type of medical image, inclusion criteria, dataset size, training network, image region of interest (ROI), and landmark quantity. Our results showed a relatively similar accuracy with Bier et al, Yang et al, and Li et al, which is 5.6, 5.19, and 5.59 mm, respectively. However, the present model had an inferior performance when compared with Zhang et al's 3.34 mm and Lee et al's 1.5 mm .…”

Section: Discussionsupporting

confidence: 91%

“…As for the inclusion criteria of training materials, some studies only used healthy subjects or the symmetric and normal medical images . The dataset size also varied between 20–40 cases, 50–70 cases, and more than 100 cases . Some studies used the pure 2D CNN, others used the 2D CNN for the 2D representations of 3D volumes, and still others used the 3D CNN directly .…”

Section: Discussionmentioning

confidence: 99%

“…With the advent of deep learning methods represented by convolutional neural networks (CNN), researchers can obtain the most descriptive characteristics of the landmarks without remarkable efforts. For example, Yang et al adopted a 2.5D approach to detect anatomical landmarks in 3D MRI scans to achieve a slice‐based image classification. Zheng et al extracted patches from 3D volumes then use two cascaded multilayer perceptrons (MLPs) to automatically locate landmarks with volumetric classification.…”

Section: Introductionmentioning

confidence: 99%

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Automatic 3D landmarking model using patch‐based deep neural networks for CT image of oral and maxillofacial surgery

Kobayashi

Fan

et al. 2020

Robotics Computer Surgery

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Background: Manual landmarking is a time consuming and highly professional work.Although some algorithm-based landmarking methods have been proposed, they lack flexibility and may be susceptible to data diversity. Methods: The CT images from 66 patients who underwent oral and maxillofacial surgery (OMS) were landmarked manually in MIMICS. Then the CT slices were exported as images for recreating the 3D volume. The coordinate data of landmarks were further processed in Matlab using a principal component analysis (PCA) method. A patch-based deep neural network model with a three-layer convolutional neural network (CNN) was trained to obtain landmarks from CT images. Results: The evaluating experiment showed that this CNN model could automatically finish landmarking in an average processing time of 37.871 seconds with an average accuracy of 5.785 mm. Conclusion: This study shows a promising potential to relieve the workload of the surgeon and reduces the dependence on human experience for OMS landmarking. K E Y W O R D S 3D cephalometry, automatic landmarking, convolutional neural network, machine learning, oral and maxillofacial surgery

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic 3D landmarking model using patch‐based deep neural networks for CT image of oral and maxillofacial surgery

Kobayashi

Fan

et al. 2020

Robotics Computer Surgery

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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%

“…To solve 3D data parsing with deep learning algorithms, several approaches have been proposed that treat the 3D space as a composition of 2D orthogonal planes. Yang et al (2015) identified landmarks on the distal femur surface by processing three independent sets of 2D MRI slices (one for each plane) with regular CNNs. The 3D position of the landmark was defined as the intersection of the three 2D slices with the highest classification output.…”

Section: Detection 321 Organ Region and Landmark Localizationmentioning

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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Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing

Zhang

Bai

Chen

et al. 2019

Concurrency and Computation

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Cloud computing is significantly contributing to the development of smart Chinese medicine. The diagnosis and treatment of spleen and stomach diseases has been arousing great interest in smart Chinese medicine with cloud computing since many persons are suffering from spleen and stomach diseases. Currently, spleen and stomach diseases present some new characteristics with the dramatic changes in natural climate, social environment, and human living habits. Recently, deep learning, together with cloud computing techniques, has successfully used in medical image analysis and therefore it is the most promising model for diagnosing spleen and stomach disease in smart Chinese medicine. In this paper, we present a survey on deep learning models in medical image analysis for computer-aided diagnosis in modern medicine. Afterwards, we summarize the syndrome types of spleen and stomach diseases and furthermore analyze the causes and pathogenesis for each syndrome. Finally, we discuss the open challenges and research directions of deep learning models applicable to the computer-aided diagnosis of spleen and stomach diseases, which is expected to contribute to the development of smart Chinese medicine with cloud computing. KEYWORDS cloud computing, deep learning, smart Chinese medicine, spleen and stomach 1 INTRODUCTION Recently, cloud computing has made a great progress as various powerful computing techniques have been combined with advanced communication techniques. 1-3 The major objective of cloud computing is to provide powerful computational functions to big data processing and machine learning models. Actually, it has become the fundamental computing infrastructure for big data processing and large-scale machine learning algorithms in the past few years. 4,5 For example, cloud computing has extensively used to store large-scale data and improve the efficiency of data mining techniques like the possibilistic c-means algorithm. Particularly, cloud computing, together with various big data analytics algorithms and machine learning models, is significantly contributing to the development of smart city, which involves smart transportation, smart industry, and smart medicine, etc. As an important part of smart medicine, smart Chinese medicine has been receiving a lot of attention from many researchers in the communities of artificial intelligence and traditional Chinese medicine. 6 In detail, smart Chinese medicine integrates the advanced artificial intelligence techniques such as deep learning into traditional Chinese medicine to enhance the diagnosis and treatment of many difficult and complicated diseases like spleen and stomach diseases. In this paper, we focus on deep learning models for diagnosing spleen and stomach diseases. The diagnosis and treatment of spleen and stomach disease is one of the core topics in smart Chinese medicine since a large number of people suffer from spleen and stomach diseases. Particularly, more than 25% of the population suffers from chronical stomach disorder. Spleen and stomach diseases...

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Automated anatomical landmark detection ondistal femur surface using convolutional neural network

Cited by 78 publications

References 13 publications

Automatic 3D landmarking model using patch‐based deep neural networks for CT image of oral and maxillofacial surgery

Automatic 3D landmarking model using patch‐based deep neural networks for CT image of oral and maxillofacial surgery

A survey on deep learning in medical image analysis

Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing

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