A Pattern Classification Approach to Aorta Calcium Scoring in Radiographs

Bruijne, Marleen de

doi:10.1007/11569541_18

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Previous studies have proposed several machine learning techniques. In 2006, Brujne et al [30,31] employed a shape model with particle filter techniques to automate AAC and evaluated its performance based on accuracy. However, the accuracy of the model can be misleading if the ratio of true negative pixels is unknown.…”

Section: Discussionmentioning

confidence: 99%

Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms

Wang,

et al. 2023

Bioengineering

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To investigate the performance of deep-learning-based algorithms for the automatic segmentation and quantification of abdominal aortic calcification (AAC) in lateral lumbar radiographs, we retrospectively collected 1359 consecutive lateral lumbar radiographs. The data were randomly divided into model development and hold-out test datasets. The model development dataset was used to develop U-shaped fully convolutional network (U-Net) models to segment the landmarks of vertebrae T12–L5, the aorta, and anterior and posterior aortic calcifications. The AAC lengths were calculated, resulting in an automatic Kauppila score output. The vertebral levels, AAC scores, and AAC severity were obtained from clinical reports and analyzed by an experienced expert (reference standard) and the model. Compared with the reference standard, the U-Net model demonstrated a good performance in predicting the total AAC score in the hold-out test dataset, with a correlation coefficient of 0.97 (p <0.001). The overall accuracy for the AAC severity was 0.77 for the model and 0.74 for the clinical report. Additionally, the Kendall coefficient of concordance of the total AAC score prediction was 0.89 between the model-predicted score and the reference standard, and 0.88 between the structured clinical report and the reference standard. In conclusion, the U-Net-based deep learning approach demonstrated a relatively high model performance in automatically segmenting and quantifying ACC.

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Section: Discussionmentioning

confidence: 99%

Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms

Wang,

et al. 2023

Bioengineering

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“…U-net segmentation also compared favourably to previous work segmenting AAC in radiographs. The performance of the shape model and particle filter techniques from DeBrujne 17,18 use accuracy as the performance metric. Although the value of accuracy can be misleading without knowing the ratio of true and negative pixels, the U-net's higher accuracy is promising.…”

Section: Discussionmentioning

confidence: 99%

Automated scoring of aortic calcification in vertebral fracture assessment images

Chaplin

Cootes

2019

Medical Imaging 2019: Computer-Aided Diagnosis

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The severity of abdominal aortic calcification (AAC) is a strong, independent predictor of cardiovascular disease (CVD). Vertebral fracture assessment (VFA) is a low radiation screening tool which can be used to incidentally measure AAC. This work compares the performance of Haar feature random forest classification with a Unet based convolutional neural network (CNN) segmentation, to automatically quantify AAC. Clinical semiquantitative scores were also generated using U-net. Scores were calculated using the relative length of labelled calcification and compared to manual scoring. The U-net outperformed the random forest, showed sensible segmentations and AAC scores, though it could not match human annotation accuracy.

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“…We therefore use a set of training images in which the aorta and the vertebrae have been annotated to model the probability density function of aorta shape and location conditional on the spine shape. In a previous paper, we showed how samples from this shape model can be combined with a model of how the calcium is distributed within the aorta to define a spatially varying calcium prior dependent on the position and shape of the spine [7]. Application of this prior improved results of standard pixel classification.…”

Section: Aortic Calcificationmentioning

confidence: 95%

Shape Particle Guided Tissue Classification

Bruijne

2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06)

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In many cases, the accuracy of statistical pixel classification can be improved by applying a spatially varying prior that can be derived from a shape model. We propose to represent the prior knowledge on the spatial distribution of tissue classes by a distribution of shape particles, each representing one possible distribution of tissue classes.Classification and shape can then be optimized jointly by alternating a particle filtering step, in which the shape particle distribution is evolved under the influence of the current classification, with an update of the classification estimate using the shape distribution. Since a large number of shape hypotheses is used this method does not easily get trapped in local maxima. By applying shape models that are conditional on other, more easily discernible, objects in the image one can perform shape guided classification even if the shapes themselves are hardly visible.The method is demonstrated on the task of detecting aortic calcifications in X-ray images, in which calcifications can only be present inside the aorta -mainly on the aortic wall -but the aorta itself is not visible.

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A Pattern Classification Approach to Aorta Calcium Scoring in Radiographs

Cited by 6 publications

References 11 publications

Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms

Automatic Segmentation and Quantification of Abdominal Aortic Calcification in Lateral Lumbar Radiographs Based on Deep-Learning-Based Algorithms

Automated scoring of aortic calcification in vertebral fracture assessment images

Shape Particle Guided Tissue Classification

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