4,912 research outputs found
An Automatic Level Set Based Liver Segmentation from MRI Data Sets
A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results
A deep level set method for image segmentation
This paper proposes a novel image segmentation approachthat integrates fully
convolutional networks (FCNs) with a level setmodel. Compared with a FCN, the
integrated method can incorporatesmoothing and prior information to achieve an
accurate segmentation.Furthermore, different than using the level set model as
a post-processingtool, we integrate it into the training phase to fine-tune the
FCN. Thisallows the use of unlabeled data during training in a
semi-supervisedsetting. Using two types of medical imaging data (liver CT and
left ven-tricle MRI data), we show that the integrated method achieves
goodperformance even when little training data is available, outperformingthe
FCN or the level set model alone
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Semiautomated 3D liver segmentation using computed tomography and magnetic resonance imaging
Le foie est un organe vital ayant une capacitĂ© de rĂ©gĂ©nĂ©ration exceptionnelle et un rĂŽle crucial dans le fonctionnement de lâorganisme. LâĂ©valuation du volume du foie est un outil important pouvant ĂȘtre utilisĂ© comme marqueur biologique de sĂ©vĂ©ritĂ© de maladies hĂ©patiques. La volumĂ©trie du foie est indiquĂ©e avant les hĂ©patectomies majeures, lâembolisation de la veine porte et la transplantation.
La méthode la plus répandue sur la base d'examens de tomodensitométrie (TDM) et d'imagerie par résonance magnétique (IRM) consiste à délimiter le contour du foie sur plusieurs coupes consécutives, un processus appelé la «segmentation».
Nous prĂ©sentons la conception et la stratĂ©gie de validation pour une mĂ©thode de segmentation semi-automatisĂ©e dĂ©veloppĂ©e Ă notre institution. Notre mĂ©thode reprĂ©sente une approche basĂ©e sur un modĂšle utilisant lâinterpolation variationnelle de forme ainsi que lâoptimisation de maillages de Laplace. La mĂ©thode a Ă©tĂ© conçue afin dâĂȘtre compatible avec la TDM ainsi que l' IRM.
Nous avons Ă©valuĂ© la rĂ©pĂ©tabilitĂ©, la fiabilitĂ© ainsi que lâefficacitĂ© de notre mĂ©thode semi-automatisĂ©e de segmentation avec deux Ă©tudes transversales conçues rĂ©trospectivement. Les rĂ©sultats de nos Ă©tudes de validation suggĂšrent que la mĂ©thode de segmentation confĂšre une fiabilitĂ© et rĂ©pĂ©tabilitĂ© comparables Ă la segmentation manuelle. De plus, cette mĂ©thode diminue de façon significative le temps dâinteraction, la rendant ainsi adaptĂ©e Ă la pratique clinique courante.
Dâautres Ă©tudes pourraient incorporer la volumĂ©trie afin de dĂ©terminer des marqueurs biologiques de maladie hĂ©patique basĂ©s sur le volume tels que la prĂ©sence de stĂ©atose, de fer, ou encore la mesure de fibrose par unitĂ© de volume.The liver is a vital abdominal organ known for its remarkable regenerative
capacity and fundamental role in organism viability. Assessment of liver volume is
an important tool which physicians use as a biomarker of disease severity. Liver
volumetry is clinically indicated prior to major hepatectomy, portal vein
embolization and transplantation.
The most popular method to determine liver volume from computed
tomography (CT) and magnetic resonance imaging (MRI) examinations involves
contouring the liver on consecutive imaging slices, a process called
âsegmentationâ. Segmentation can be performed either manually or in an
automated fashion.
We present the design concept and validation strategy for an innovative
semiautomated liver segmentation method developed at our institution. Our
method represents a model-based approach using variational shape interpolation
and Laplacian mesh optimization techniques. It is independent of training data,
requires limited user interactions and is robust to a variety of pathological cases.
Further, it was designed for compatibility with both CT and MRI examinations.
We evaluated the repeatability, agreement and efficiency of our
semiautomated method in two retrospective cross-sectional studies. The results of
our validation studies suggest that semiautomated liver segmentation can provide
strong agreement and repeatability when compared to manual segmentation.
Further, segmentation automation significantly shortens interaction time, thus
making it suitable for daily clinical practice.
Future studies may incorporate liver volumetry to determine volume-averaged
biomarkers of liver disease, such as such as fat, iron or fibrosis measurements per
unit volume. Segmental volumetry could also be assessed based on
subsegmentation of vascular anatomy
Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks
A cascade of fully convolutional neural networks is proposed to segment
multi-modal Magnetic Resonance (MR) images with brain tumor into background and
three hierarchical regions: whole tumor, tumor core and enhancing tumor core.
The cascade is designed to decompose the multi-class segmentation problem into
a sequence of three binary segmentation problems according to the subregion
hierarchy. The whole tumor is segmented in the first step and the bounding box
of the result is used for the tumor core segmentation in the second step. The
enhancing tumor core is then segmented based on the bounding box of the tumor
core segmentation result. Our networks consist of multiple layers of
anisotropic and dilated convolution filters, and they are combined with
multi-view fusion to reduce false positives. Residual connections and
multi-scale predictions are employed in these networks to boost the
segmentation performance. Experiments with BraTS 2017 validation set show that
the proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for
enhancing tumor core, whole tumor and tumor core, respectively. The
corresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and
0.7748, respectively.Comment: 12 pages, 5 figures. MICCAI Brats Challenge 201
A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function
Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term
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