Proceedings ArticleDOI
Segmentation of Vascular Regions in Ultrasound Images: A Deep Learning Approach
Reads0
Chats0
TLDR
A pipelined network comprising of a convolutional neural network followed by unsupervised clustering is proposed to perform vessel segmentation in liver ultrasound images, motivated by the tremendous success of CNNs in object detection and localization.Abstract:
Vascular region segmentation in ultrasound images is necessary for applications like automatic registration, and surgical navigation. In this paper, a pipelined network comprising of a convolutional neural network (CNN) followed by unsupervised clustering is proposed to perform vessel segmentation in liver ultrasound images. The work is motivated by the tremendous success of CNNs in object detection and localization. CNN here is trained to localize vascular regions, which are subsequently segmented by the clustering. The proposed network results in 99.14% pixel accuracy and 69.62% mean region intersection over union on 132 images. These values are better than some existing methods.read more
Citations
More filters
Posted Content
W-Net: Dense Semantic Segmentation of Subcutaneous Tissue in Ultrasound Images by Expanding U-Net to Incorporate Ultrasound RF Waveform Data.
Gautam Rajendrakumar Gare,Jiayuan Li,Rohan Joshi,Mrunal Prashant Vaze,Rishikesh Magar,Michael Yousefpour,Ricardo Luis Rodriguez,John Galeotti +7 more
TL;DR: W-Net is presented, a novel Convolution Neural Network framework that employs raw ultrasound waveforms from each A-scan, typically referred to as ultrasound Radio Frequency data, in addition to the gray ultrasound image to semantically segment and label tissues, and is the first deep-learning or CNN approach for segmentation that analyses ultrasound raw RF data along with the gray image.
Journal ArticleDOI
AI-based Ultrasound Imaging Technologies for Hepatic Diseases
TL;DR: In this article , a review summarizes the current rapid development of US technology and related AI methods in the diagnosis and treatment of hepatic diseases, including steatosis grading, fibrosis staging, detection of focal liver lesions, US image segmentation, multimodal image registration, and other applications.
Book ChapterDOI
Automatic Finger Tendon Segmentation from Ultrasound Images Using Deep Learning
TL;DR: This work proposes a finger tendon segmentation CNN which overcomes the requirement of prior knowledge and gives promising results on ultrasound images, and is the first deep learning finger tendon segmentsation method from transverse ultrasound images.
Dissertation
Automated vascular region segmentation in ultrasound to utilize surgical navigation in liver surgery
TL;DR: Given that the ultrasound acquisitions do not contain the vena cava or gallbladder, and span a large part of the hepatic vasculature, the approach looks promising and further optimization of automatically acquiring similar point clouds is expected to stimulate the adaptation of surgical navigation.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.
Posted Content
Rich feature hierarchies for accurate object detection and semantic segmentation
TL;DR: This paper proposes a simple and scalable detection algorithm that improves mean average precision (mAP) by more than 30% relative to the previous best result on VOC 2012 -- achieving a mAP of 53.3%.