Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks

Abstract

Abstract. Deep convolutional neural networks have achieved great results on image classification problems. In this paper, a new method using a deep convolutional neural network for detecting blood vessels in Bmode ultrasound images is presented. Automatic blood vessel detection may be useful in medical applications such as deep venous thrombosis detection, anesthesia guidance and catheter placement. The proposed method is able to determine the position and size of the vessels in images in real-time. 12,804 subimages of the femoral region from 15 subjects were manually labeled. Leave-one-subject-out cross validation was used giving an average accuracy of 94.5%, a major improvement from previous methods which had an accuracy of 84% on the same dataset. The method was also validated on a dataset of the carotid artery to show that the method can generalize to blood vessels on other regions of the body. The accuracy on this dataset was 96%.

A survey on deep learning in medical image analysis

“…Several researchers have shown that designing architectures incorporating unique task-specific properties can obtain better results than straightforward CNNs. Two examples which we encountered several times are multi-view Gao et al (2016d) Frame labeling US CNN 4 class frame classification using transfer learning with pre-trained networks Kumar et al (2016) Frame labeling US CNN 12 standard anatomical planes, CNN extracts features for support vector machine Rajchl et al (2016b) Segmentation with non expert labels MRI CNN Crowd-sourcing annotation efforts to segment brain structures Rajchl et al (2016a) Segmentation given bounding box MRI CNN CNN and CRF for segmentation of structures Ravishankar et al (2016a) Quantification US CNN Hybrid system using CNN and texture features to find abdominal circumference Yu et al (2016b) Left ventricle segmentation US CNN Frame-by-frame segmentation by dynamically fine-tuning CNN to the latest frame Wound segmentation photographs CNN Additional detection of infection risk and healing progress Ypsilantis et al (2015) Chemotherapy response prediction PET CNN CNN outperforms classical radiomics features in patients with esophageal cancer Zheng et al (2015) Carotid artery bifurcation detection CT CNN Two stage detection process, CNNs combined with Haar features Alansary et al (2016) Placenta segmentation MRI CNN 3D multi-stream CNN with extension for motion correction Fritscher et al (2016) Head&Neck tumor segmentation CT CNN 3 orthogonal patches in 2D CNNs, combined with other features Jaumard- Hakoun et al (2016) Tongue contour extraction US RBM Analysis of tongue motion during speech, combines auto-encoders with RBMs Payer et al (2016) Hand landmark detection X-ray CNN Various architectures are compared Quinn et al (2016) Disease detection microscopy CNN Smartphone mounted on microscope detects malaria, tuberculosis & parasite eggs Smistad and Løvstakken (2016) Vessel detection and segmentation US CNN Femoral and carotid vessels analyzed with standard fCNN Twinanda et al (2017) Task recognition in laparoscopy Videos CNN Fine-tuned AlexNet applied to video frames Xu et al (2016c) Cervical dysplasia cervigrams CNN Fine-tuned pre-trained network with added non-imaging features Xue et al (2016) Esophageal microvessel classification Microscopy CNN Simple CNN used for feature extraction Zhang et al (2016a) Image reconstruction CT CNN Reconstructing from limited angle measurements, reducing reconstruction artefacts Lekadir et al (2017) Carotid plaque classification US CNN Simple CNN for characterization of carotid plaque composition in ultrasound …”

Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics

“…On the other side, CNNs are trained to directly obtain vascular segmentation in [51] for retinal vessel segmentation in OCT angiography, in [38] for carotid segmentation in ultrasound images and in [47] for retinal segmentation in color fundus photography images. Specifically, the CNN fully connected layer is used to classify each pixel in the image as belonging to vessel or background.…”

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Automated 3D geometry segmentation of the healthy and diseased carotid artery in free‐hand, probe tracked ultrasound images

“…By coupling the Star‐Kalman algorithm to probe tracking information, the algorithm is able to also correct for unpredictable probe movements by the sonographer. Most artery detection algorithms rely on an ellipse/circle fit . To find the true lumen–intima/wall border active contour algorithms (snakes) can be used .…”

“…A possible solution is to combine multiple sweeps to obtain sufficient contrast over the full length of the carotid artery. Deep‐learning approaches are a promising tool for medical image segmentation . developed a deep convolutional neural network that was able to classify blood vessels in B‐mode ultrasound images.…”