scholarly journals Registration-Based Organ Positioning and Joint Segmentation Method for Liver and Tumor Segmentation

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Huiyan Jiang ◽  
Shaojie Li ◽  
Siqi Li
Keyword(s):  
Active Contour Model ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Initial Contour ◽  
Tumor Segmentation ◽  
Segmentation Method ◽  
Anatomical Structures ◽  
Low Contrast ◽  
Medical Diagnoses ◽  
Learning Machine

The automated segmentation of liver and tumor from CT images is of great importance in medical diagnoses and clinical treatment. However, accurate and automatic segmentation of liver and tumor is generally complicated due to the complex anatomical structures and low contrast. This paper proposes a registration-based organ positioning (ROP) and joint segmentation method for liver and tumor segmentation from CT images. First, a ROP method is developed to obtain liver’s bounding box accurately and efficiently. Second, a joint segmentation method based on fuzzy c-means (FCM) and extreme learning machine (ELM) is designed to perform coarse liver segmentation. Third, the coarse segmentation is regarded as the initial contour of active contour model (ACM) to refine liver boundary by considering the topological information. Finally, tumor segmentation is performed using another ELM. Experiments on two datasets demonstrate the performance advantages of our proposed method compared with other related works.

scholarly journals 3D IFPN: Improved Feature Pyramid Network for Automatic Segmentation of Gastric Tumor

2021 ◽  
Vol 11 ◽  
Author(s):  
Haimei Li ◽  
Bing Liu ◽  
Yongtao Zhang ◽  
Chao Fu ◽  
Xiaowei Han ◽  
...  
Keyword(s):  
Liver Tumor ◽  
Feature Fusion ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Learning Ability ◽  
Gastric Tumor ◽  
Tumor Segmentation ◽  
Gastric Tumors ◽  
Multi Level ◽  
Liver Tumor Segmentation

Automatic segmentation of gastric tumor not only provides image-guided clinical diagnosis but also assists radiologists to read images and improve the diagnostic accuracy. However, due to the inhomogeneous intensity distribution of gastric tumors in CT scans, the ambiguous/missing boundaries, and the highly variable shapes of gastric tumors, it is quite challenging to develop an automatic solution. This study designs a novel 3D improved feature pyramidal network (3D IFPN) to automatically segment gastric tumors in computed tomography (CT) images. To meet the challenges of this extremely difficult task, the proposed 3D IFPN makes full use of the complementary information within the low and high layers of deep convolutional neural networks, which is equipped with three types of feature enhancement modules: 3D adaptive spatial feature fusion (ASFF) module, single-level feature refinement (SLFR) module, and multi-level feature refinement (MLFR) module. The 3D ASFF module adaptively suppresses the feature inconsistency in different levels and hence obtains the multi-level features with high feature invariance. Then, the SLFR module combines the adaptive features and previous multi-level features at each level to generate the multi-level refined features by skip connection and attention mechanism. The MLFR module adaptively recalibrates the channel-wise and spatial-wise responses by adding the attention operation, which improves the prediction capability of the network. Furthermore, a stage-wise deep supervision (SDS) mechanism and a hybrid loss function are also embedded to enhance the feature learning ability of the network. CT volumes dataset collected in three Chinese medical centers was used to evaluate the segmentation performance of the proposed 3D IFPN model. Experimental results indicate that our method outperforms state-of-the-art segmentation networks in gastric tumor segmentation. Moreover, to explore the generalization for other segmentation tasks, we also extend the proposed network to liver tumor segmentation in CT images of the MICCAI 2017 Liver Tumor Segmentation Challenge.

scholarly journals A Combined Deep-Learning and Lattice Boltzmann Model for Segmentation of the Hippocampus in MRI

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3628
Author(s):  
Yingqian Liu ◽  
Zhuangzhi Yan
Keyword(s):  
Lattice Boltzmann ◽  
Active Contour Model ◽  
Input Image ◽  
Lattice Boltzmann Model ◽  
Patient Specific ◽  
Initial Contour ◽  
Shape Prior ◽  
Low Contrast ◽  
Specific Shape ◽  
Low Efficiency

Segmentation of the hippocampus (HC) in magnetic resonance imaging (MRI) is an essential step for diagnosis and monitoring of several clinical situations such as Alzheimer’s disease (AD), schizophrenia and epilepsy. Automatic segmentation of HC structures is challenging due to their small volume, complex shape, low contrast and discontinuous boundaries. The active contour model (ACM) with a statistical shape prior is robust. However, it is difficult to build a shape prior that is general enough to cover all possible shapes of the HC and that suffers the problems of complicated registration of the shape prior and the target object and of low efficiency. In this paper, we propose a semi-automatic model that combines a deep belief network (DBN) and the lattice Boltzmann (LB) method for the segmentation of HC. The training process of DBN consists of unsupervised bottom-up training and supervised training of a top restricted Boltzmann machine (RBM). Given an input image, the trained DBN is utilized to infer the patient-specific shape prior of the HC. The specific shape prior is not only used to determine the initial contour, but is also introduced into the LB model as part of the external force to refine the segmentation. We used a subset of OASIS-1 as the training set and the preliminary release of EADC-ADNI as the testing set. The segmentation results of our method have good correlation and consistency with the manual segmentation results.

AUTOMATIC MULTI-SCALE SEGMENTATION OF INTRAHEPATIC VESSEL IN CT IMAGES FOR LIVER SURGERY PLANNING

2013 ◽  
Vol 27 (01) ◽  
pp. 1357001 ◽  
Author(s):  
YI WANG ◽  
BIN FANG ◽  
JINGRUI PI ◽  
LEI WU ◽  
PATRICK S. P. WANG ◽  
...  
Keyword(s):  
Distribution Function ◽  
Blood Vessels ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Hessian Matrix ◽  
Ct Images ◽  
Vascular Pattern ◽  
Low Contrast ◽  
Multi Scale ◽  
Normal Scale

The processing of blood vessels is an indispensable part in complicated surgeries of livers and hearts as the development of medical image technologies, which requires an automatic segmentation system over CT images of organs. However, the vascular pattern of livers in CT images suffers from low contrast to background so that the existing segmentation technologies are not able to extract the blood vessels completely. In the paper, we propose a new algorithm to extract the blood vessels of livers based on the adaptive multi-scale segmentation. First, we prove that the background histogram of normal scale blood vessels obeys the Gaussian distribution in CT images, and obtain the vascular distribution function from the vascular signal segmented from the background with a local optimal threshold. Second, Hessian matrix is employed to enhance the thin blood vessels before the extraction, and a complete and clear segmentation system for blood vessels is constructed by combining the major and thin blood vessels via filtering. Experimental results show the effectiveness of the proposed method, which is able to extract more complete blood vessels for 3D system, and assist the clinical liver surgeries efficiently.

An Intelligent Image Segmentation Algorithm for Medical Ultrasonic Instruments

2009 ◽  
Vol 419-420 ◽  
pp. 701-704
Author(s):  
Guo Chang Gu ◽  
Chang Ming Zhu ◽  
Hai Bo Liu ◽  
Sheng Jing ◽  
Hua Long Yu
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Active Contour Model ◽  
Relevance Vector Machine ◽  
Initial Contour ◽  
Ultrasound Images ◽  
Qualitative And Quantitative Analysis ◽  
Segmentation Method ◽  
Ultrasonic Images ◽  
Image Segmentation Algorithm

In company with medical instruments ' development, the corresponding software plays more and more role in the application. And medical image processing software has become an important component of the medical ultrasonic instruments. Image segmentation plays an important role in both qualitative and quantitative analysis of medical ultrasound images. But state-of-arts methods in the aspect of segmentation can not get satisfactory results. We propose an intelligent image segmentation method for medical ultrasonic images. The algorithm improved active contour model with relevance vector machine, where the advantages of supervised learning classification and the global region distribution information can be exploited to enhance the performance. In order to improve the segmentation speed and get precise initial contour, relevance vector machine also is used to obtain initial contour firstly. A large amount of experimental results have proved that our method outperforms many state-of-arts methods in the aspect of segmentation, and the method can be used in ultrasonic instruments effectively.

Automatic MRI Brain Tumor Segmentation System Based on Localizing Active Contour Models

2011 ◽  
Vol 219-220 ◽  
pp. 1342-1346 ◽  
Author(s):  
Ying Wang ◽  
Zhi Xian Lin ◽  
Jian Guo Cao ◽  
Mao Qing Li
Keyword(s):  
Brain Tumor ◽  
Active Contour ◽  
Automatic Segmentation ◽  
Brain Mri ◽  
Initial Contour ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Active Contour Models ◽  
Mri Brain ◽  
Contour Models

In this paper, an automatic segmentation system was developed for MRI brain tumor. Local region-based active contour models were suitable for heterogeneous features of brain MRI image. But the models are sensitive to initial contour, which generally requires manual setting. An automatic MRI brain tumor segmentation system were developed based on localized contour models, which can identify tumor-dominant slice, set initial contour automatically and segment tumor’s contours from all MRI slices autonomously. K-means clustering and grayscale analysis were combined to identify tumor-dominant slice. Multi-threshold algorithm with the aid of erosion and dilation operators was adopted to obtain an initial contour for the tumor-dominant slice. The segmentation contour from the local active contour models was applied as initial contours of two-side neighboring slices. MRI brain tumor data were applied to validate the automatic segmentation system.

An Effective X-Ray Image Segmentation Method for the Pharmaceutical Industry via Surface Fitting

2013 ◽  
Vol 333-335 ◽  
pp. 839-844
Author(s):  
Kai Hong Shi ◽  
Zong Qing Lu ◽  
Qing Min Liao
Keyword(s):  
Image Segmentation ◽  
Pharmaceutical Industry ◽  
Active Contour Model ◽  
Three Dimensional ◽  
Surface Fitting ◽  
Segmentation Method ◽  
Image Subtraction ◽  
Low Contrast ◽  
X Ray ◽  
Fixed Condition

Image segmentation techniques currently used for X-ray inspection in pharmaceutical industry suffer from some limitations. The object in an image is close to the background and its contours are weak or blurred because of the X-ray imaging characteristic. Based on our research of X-ray inspection, a simple and efficient image segmentation method is proposed in this paper. It is implemented by treating the image and desired contours as three dimensional surface and holes respectively in order to simplify the model of segmentation, and making use of surface fitting and image subtraction to extract the target region efficiently. The novelty of this approach is that we need less selection of parameters to extract contours with low contrast by surface fitting. Experiments on real X-ray images demonstrate the advantages of the proposed method over active contour model (ACM) and Chan_Vese model (CV model) in terms of both accuracy and efficiency on fixed condition.

Sign in / Sign up

Export Citation Format

Share Document