Probabilistic boosting trees for automatic bone removal from CT angiography images

When visualizing vessels with CT angiography scans, the arteries are often obstructed by bones. Traditional methods require an additional non-enhanced scan to acquire a bone mask which is then subtracted from the original CTA scan. In this study, we present an automated bone removal method using only contrast enhanced scans based on simultaneous label fusion. We build an atlas database where each atlas is paired with a bone label and a vessel label. After the atlases are mapped to a subject, we propose a vessel preserving scheme to protect possible vessel areas from bone removal by simultaneous label fusion. Seven clinical data sets were used for validation and the results showed that this method can achieve consistent and thorough bone removal with maximal vessels preservation.

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Machine learning-based approach for segmentation of intervertebral disc degeneration from lumbar section of spine using MRI images

Shinde¹,

Joshi²,

Manthalkar³

et al. 2021

Bio-Algorithms and Med-Systems

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Objectives Intervertebral disc segmentation is one of the methods to diagnose spinal disease through the degeneration in asymptomatic and symptomatic patients. Even though numerous intervertebral disc segmentation techniques are available, classifying the grades in the intervertebral disc is a hectic challenge in the existing disc segmentation methods. Thus, an effective Whale Spine-Generative Adversarial Network (WSpine-GAN) method is proposed to segment the intervertebral disc for effective grade classification. Methods The proposed WSpine-GAN method effectively performs the disc segmentation, wherein the weights of Spine-GAN are optimally tuned using Whale Optimization Algorithm (WOA). Then, the refined disc features, such as pixel-based features and the connectivity features are extracted. Finally, the K-Nearest Neighbor (KNN) classifier based on the pfirrmann’s grading system performs the grade classification. Results The implementation of the grade classification strategy based on the proposed WSpine-GAN and KNN is performed using the real-time database, and the performance based on the metrics yielded the accuracy, true positive rate (TPR), and false positive rate (FPR) values of 97.778, 97.83, and 0.586% for the training percentage and 92.382, 90.580, and 1.972% for the K-fold value. Conclusions The proposed WSpine-GAN method effectively performs the disc segmentation by integrating the Spine-GANmethod and WOA. Here, the spinal cord images are segmented using the proposed WSpine-GAN method by tuning the weights optimally to enhance the performance of the disc segmentation.

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Probabilistic boosting trees for automatic bone removal from CT angiography images

Cited by 3 publications

References 14 publications

An efficient automatic segmentation of spinal cord in MRI images using interactive random walker (RW) with artificial bee colony (ABC) algorithm

An efficient automatic segmentation of spinal cord in MRI images using interactive random walker (RW) with artificial bee colony (ABC) algorithm

Simultaneous label fusion with vessel preserving for bone removal in CT angiography scans

Machine learning-based approach for segmentation of intervertebral disc degeneration from lumbar section of spine using MRI images

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