Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Prasoon, Adhish; Petersen, Kathrine Birch; Igel, Christian; Lauze, François; Dam, Erik B.; Nielsen, Mads

doi:10.1007/978-3-642-40763-5_31

Cited by 443 publications

(327 citation statements)

References 7 publications

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“…Others have used them in conjunction with terrestrial LiDAR to map obstacles for autonomous cars [37,38]. One common application has been to identify malignancies using 3D medical scans [39][40][41]. Several studies have also used 3D CNNs for household object detection [42][43][44].…”

Section: Introductionmentioning

confidence: 99%

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Ayrey

Hayes

2018

Remote Sensing

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Abstract:As light detection and ranging (LiDAR) technology becomes more available, it has become common to use these datasets to generate remotely sensed forest inventories across landscapes. Traditional methods for generating these inventories employ the use of height and proportion metrics to measure LiDAR returns and relate these back to field data using predictive models. Here, we employ a three-dimensional convolutional neural network (CNN), a deep learning technique that scans the LiDAR data and automatically generates useful features for predicting forest attributes. We test the accuracy in estimating forest attributes using the three-dimensional implementations of different CNN models commonly used in the field of image recognition. Using the best performing model architecture, we compared CNN performance to models developed using traditional height metrics. The results of this comparison show that CNNs produced 12% less prediction error when estimating biomass, 6% less in estimating tree count, and 2% less when estimating the percentage of needleleaf trees. We conclude that using CNNs can be a more accurate means of interpreting LiDAR data for forest inventories compared to standard approaches.

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Section: Introductionmentioning

confidence: 99%

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Ayrey

Hayes

2018

Remote Sensing

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“…As a result, deep CNN has been applied to medical image segmentation recently. For instance, knee cartilage segmentation using deep CNN showed better performance than previous methods based on handcrafted features (Prasoon et al, 2013). Brain tissues have been accurately segmented into gray matter, white matter and cerebrospinal fluid using deep CNN (Moeskops et al, 2016;Zhang et al, 2015).…”

Section: Fig 1 Overview Of the Proposed Segmentation Method Two Sementioning

confidence: 99%

Fast and robust segmentation of the striatum using deep convolutional neural networks

Choi¹,

Jin

2016

Journal of Neuroscience Methods

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h i g h l i g h t s• We describe a new method for stratium segmentation.• We employ two serial deep convolutional neural networks (CNN).• Segmentation accuracy of deep CNN is comparable with that of previous methods.• The processing time of the new method is much faster than previous methods. a r t i c l e i n f o a b s t r a c tBackground: Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). New method: T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transformation. We employed two serial CNN, Global and Local CNN: The Global CNN determined approximate locations of the striatum. It performed a regression of input MR images fitted to smoothed segmentation maps of the striatum. From the output volume of Global CNN, cropped MR volumes which included the striatum were extracted. The cropped MR volumes and the output volumes of Global CNN were used for inputs of Local CNN. Local CNN predicted the accurate label of all voxels. Segmentation results were compared with a widely used segmentation method, FreeSurfer. Results: Our method showed higher Dice Similarity Coefficient (DSC) (0.893 ± 0.017 vs. 0.786 ± 0.015) and precision score (0.905 ± 0.018 vs. 0.690 ± 0.022) than FreeSurfer-based striatum segmentation (p = 0.06). Our approach was also tested using another independent dataset, which showed high DSC (0.826 ± 0.038) comparable with that of FreeSurfer.Comparison with existing method Segmentation performance of our proposed method was comparable with that of FreeSurfer. The running time of our approach was approximately three seconds. Conclusion: We suggested a fast and accurate deep CNN-based segmentation for small brain structures which can be widely applied to brain image analysis.

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“…Deep learning is also been used to grade nuclear cataracts [55]. Deep learning is been widely used in medical image processing for segmentation, classification and registration [56][57][58][59][60][61], image denoising [62] and multimodal learning [63]. Deep learning is proved to give robust image representation for single training sample per person in face recognition task [64].…”

Section: Related Workmentioning

confidence: 99%

A novel training algorithm for convolutional neural network

Anuse¹,

Vyas²

2016

Complex Intell. Syst.

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Many machine learning softwares are available which help the researchers to accomplish various tasks. These software packages have various conventional algorithms which perform well if the training and test data are independent and identically distributed. However, this might not be the case in the real world. The training data may not be available at one time. In the case of neural networks, the architecture has to be retrained with new data that are made available subsequently. In this paper, we present a novel training algorithm which can avoid complete retraining of any neural network architecture meant for visual pattern recognition. To show the utility of the algorithm, we have investigated the performance of convolutional neural network (CNN) architecture for a face recognition task under transfer learning. The proposed training algorithm may be used for enhancing the utility of machine learning software by providing researchers with an approach that can reduce the training time under transfer learning.

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Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Cited by 443 publications

References 7 publications

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory

Fast and robust segmentation of the striatum using deep convolutional neural networks

A novel training algorithm for convolutional neural network

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