Proceedings ArticleDOI
Segmentation of Vascular Regions in Ultrasound Images: A Deep Learning Approach
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
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Journal ArticleDOI
Automated vessel segmentation in lung CT and CTA images via deep neural networks.
Wenjun Tan,Wenjun Tan,luyu zhou,luyu zhou,Xiaoshuo Li,Xiaoshuo Li,xiaoyu yang,Yufei Chen,Jinzhu Yang,Jinzhu Yang +9 more
TL;DR: Wang et al. as discussed by the authors reviewed 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluated and compared their performances in terms of Dice coefficient, over segmentation rate and under segmentation performance.
Book ChapterDOI
MR-to-US Registration Using Multiclass Segmentation of Hepatic Vasculature with a Reduced 3D U-Net.
Bart R. Thomson,Jasper N. Smit,Oleksandra Ivashchenko,Niels F. M. Kok,Koert F. D. Kuhlmann,T.J.M. Ruers,Matteo Fusaglia +6 more
TL;DR: A workflow consisting of multi-class segmentation combined with selective non-rigid registration that leads to sufficient accuracy for integration in computer assisted liver surgery is developed using a reduced 3D U-Net for segmentation, followed by non- Rigid coherent point drift (CPD) registration.
Proceedings ArticleDOI
Vessel lumen segmentation in internal carotid artery ultrasounds with deep convolutional neural networks
TL;DR: This work represents a first successful step towards the automated identification of the vessel lumen in carotid artery ultrasound images and is an important first step in creating a system that can independently evaluate carOTid ultrasounds.
Journal ArticleDOI
Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions
Shanmugapriya Survarachakan,Pravda Jith Ray Prasad,Rabia Naseem,Javier Pérez de Frutos,Rahul Prasanna Kumar,Thomas Langø,Faouzi Alaya Cheikh,Ole Jakob Elle,Frank Lindseth +8 more
TL;DR: In this paper , the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system.
Journal ArticleDOI
A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls.
Chenhui Xiao,Zhenzhou Li,Jianfeng Lu,Jinyan Wang,Haoteng Zheng,Bi Zuyue,Mengyang Chen,Rui Mao,Minhua Lu +8 more
TL;DR: This study proposed a new method based on deep learning (DL) to track the displacement of the vessel wall from the ultrasound radio-frequency (RF) signals, which is a key technique to achieve quantitative measurement of vascular biomechanics.
References
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Posted Content
Theano: A Python framework for fast computation of mathematical expressions
Rami Al-Rfou,Guillaume Alain,Amjad Almahairi,Christof Angermueller,Dzmitry Bahdanau,Nicolas Ballas,Frédéric Bastien,Justin Bayer,Anatoly Belikov,Alexander Belopolsky,Yoshua Bengio,Arnaud Bergeron,James Bergstra,Valentin Bisson,Josh Bleecher Snyder,Nicolas Bouchard,Nicolas Boulanger-Lewandowski,Xavier Bouthillier,Alexandre de Brébisson,Olivier Breuleux,Pierre Luc Carrier,Kyunghyun Cho,Jan Chorowski,Paul F. Christiano,Tim Cooijmans,Marc-Alexandre Côté,Myriam Côté,Aaron Courville,Yann N. Dauphin,Olivier Delalleau,Julien Demouth,Guillaume Desjardins,Sander Dieleman,Laurent Dinh,Mélanie Ducoffe,Vincent Dumoulin,Samira Ebrahimi Kahou,Dumitru Erhan,Ziye Fan,Orhan Firat,Mathieu Germain,Xavier Glorot,Ian Goodfellow,Matthew M. Graham,Caglar Gulcehre,Philippe Hamel,Iban Harlouchet,Jean-Philippe Heng,Balázs Hidasi,Sina Honari,Arjun Jain,Sébastien Jean,Kai Jia,Mikhail Korobov,Vivek Kulkarni,Alex Lamb,Pascal Lamblin,Eric Larsen,César Laurent,Sean Lee,Simon Lefrancois,Simon Lemieux,Nicholas Léonard,Zhouhan Lin,Jesse A. Livezey,Cory Lorenz,Jeremiah Lowin,Qianli Ma,Pierre-Antoine Manzagol,Olivier Mastropietro,Robert T. McGibbon,Roland Memisevic,Bart van Merriënboer,Vincent Michalski,Mehdi Mirza,Alberto Orlandi,Chris Pal,Razvan Pascanu,Mohammad Pezeshki,Colin Raffel,Daniel Renshaw,Matthew Rocklin,Adriana Romero,Markus Roth,Peter Sadowski,John Salvatier,François Savard,Jan Schlüter,John Schulman,Gabriel Schwartz,Iulian Vlad Serban,Dmitriy Serdyuk,Samira Shabanian,Étienne Simon,Sigurd Spieckermann,S. Ramana Subramanyam,Jakub Sygnowski,Jérémie Tanguay,Gijs van Tulder,Joseph Turian,Sebastian Urban,Pascal Vincent,Francesco Visin,Harm de Vries,David Warde-Farley,Dustin J. Webb,Matthew Willson,Kelvin Xu,Lijun Xue,Li Yao,Saizheng Zhang,Ying Zhang +111 more
TL;DR: The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
Journal ArticleDOI
Ultrasound image segmentation: a survey
J.A. Noble,Djamal Boukerroui +1 more
TL;DR: This paper reviews ultrasound segmentation methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images, and presents a classification of methodology in terms of use of prior information.
Journal ArticleDOI
Nonlocal Means-Based Speckle Filtering for Ultrasound Images
TL;DR: Results on real images demonstrate that the proposed adaptation of the nonlocal (NL)-means filter for speckle reduction in ultrasound (US) images is able to preserve accurately edges and structural details of the image.
Journal ArticleDOI
Real-Time Vessel Segmentation and Tracking for Ultrasound Imaging Applications
TL;DR: A method for vessel segmentation and tracking in ultrasound images using Kalman filters is presented, and results indicate that mean errors between segmented contours and expert tracings are on the order of 1%-2% of the maximum feature dimension.
Journal ArticleDOI
Alignment of sparse freehand 3-D ultrasound with preoperative images of the liver using models of respiratory motion and deformation
TL;DR: A method for alignment of an interventional plan to optically tracked two-dimensional intraoperative ultrasound (US) images of the liver to enable the accurate transfer of information from three-dimensional preoperative imaging modalities to intraoperative US to aid needle placement for thermal ablation of liver metastases.