scholarly journals Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yuhan Yang ◽  
Shoujun Zhou ◽  
Peng Shang ◽  
En Qi ◽  
Shibin Wu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Active Contour ◽  
Ct Images ◽  
Deformable Registration ◽  
Initial Contour ◽  
Ct Image ◽  
Speeded Up Robust Features ◽  
Contour Propagation ◽  
Feature Based ◽  
Automatic Contour

Accurate target delineation of CT image is a critical step in radiotherapy treatment planning. This paper describes a novel strategy for automatic contour propagation, based on deformable registration, for CT images of lung cancer. The proposed strategy starts with a manual-delineated contour in one slice of a 3D CT image. By means of feature-based deformable registration, the initial contour in other slices of the image can be propagated automatically, and then refined by active contour approach. Three algorithms are employed in the strategy: the Speeded-Up Robust Features (SURF), Thin-Plate Spline (TPS), and an adapted active contour (Snake), used to refine and modify the initial contours. Five pulmonary cancer cases with about 400 slices and 1000 contours have been used to verify the proposed strategy. Experiments demonstrate that the proposed strategy can improve the segmentation performance in the pulmonary CT images. Jaccard similarity (JS) mean is about 0.88 and the maximum of Hausdorff distance (HD) is about 90%. In addition, delineation time has been considerably reduced. The proposed feature-based deformable registration method in the automatic contour propagation improves the delineation efficiency significantly.

SU-E-J-97: Recursive Contour Propagation Using Feature-Based Deformable Registration for Lung Cancer

2013 ◽  
Vol 40 (6Part8) ◽  
pp. 172-172 ◽  
Author(s):  
Y Yang ◽  
Y Xie ◽  
R Li ◽  
S Yu ◽  
M An ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deformable Registration ◽  
Contour Propagation ◽  
Feature Based

scholarly journals A Comparative Study of Auto-contouring Softwares in Delineation of Organs at Risk in Lung Cancer and Rectal Cancer

2021 ◽  
Author(s):  
weijun chen ◽  
Cheng Wang ◽  
Wenming Zhan ◽  
Yongshi Jia ◽  
Fangfang Ruan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
At Risk ◽  
Rectal Cancer ◽  
Organs At Risk ◽  
Similarity Index ◽  
Slight Modification ◽  
Ct Images ◽  
Target Area ◽  
Ct Image ◽  
Significant Difference

Abstract Background:Radiotherapy requires the target area and the organs at risk to be contoured on the CT image of the patient. During the process of organs-at-Risk (OAR) of the chest and abdomen, the doctor needs to contour at each CT image. The delineations of large and varied shapes are time-consuming and laborious.This study aims to evaluate the results of two automatic contouring software on OAR definition of CT images of lung cancer and rectal cancer patients. Methods: The CT images of 15 patients with rectal cancer and 15 patients with lung cancer were selected separately, and the organs at risk were outlined by the same experienced doctor as references, and then the same datasets were automatically contoured based on AiContour®© (Manufactured by Linking MED, China) and Raystation®© (Manufactured by Raysearch, Sweden) respectively. Overlap index (OI), Dice similarity index (DSC) and Volume difference (DV) were evaluated based on the auto-contours, and independent-sample t-test analysis is applied to the results. Results: The results of AiContour®© on OI and DSC were better than that of Raystation®© with statistical difference. There was no significant difference in DV between the results of two software. Conclusions: With AiContour®©, auto-contouring results of most organs in the chest and abdomen are good, and with slight modification, it can meet the clinical requirements for planning. With Raystation®©, auto-contouring results in most OAR is not as good as AiContour®©, and only the auto-contouring results of some organs can be used clinically after modification.

scholarly journals A Hybrid Method for Pancreas Extraction from CT Image Based on Level Set Methods

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Huiyan Jiang ◽  
Hanqing Tan ◽  
Hiroshi Fujita
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Negative Impact ◽  
Level Set Methods ◽  
Region Growing ◽  
Ct Images ◽  
Medical Image Segmentation ◽  
Initial Contour ◽  
Ct Image ◽  
Fast Marching

This paper proposes a novel semiautomatic method to extract the pancreas from abdominal CT images. Traditional level set and region growing methods that request locating initial contour near the final boundary of object have problem of leakage to nearby tissues of pancreas region. The proposed method consists of a customized fast-marching level set method which generates an optimal initial pancreas region to solve the problem that the level set method is sensitive to the initial contour location and a modified distance regularized level set method which extracts accurate pancreas. The novelty in our method is the proper selection and combination of level set methods, furthermore an energy-decrement algorithm and an energy-tune algorithm are proposed to reduce the negative impact of bonding force caused by connected tissue whose intensity is similar with pancreas. As a result, our method overcomes the shortages of oversegmentation at weak boundary and can accurately extract pancreas from CT images. The proposed method is compared to other five state-of-the-art medical image segmentation methods based on a CT image dataset which contains abdominal images from 10 patients. The evaluated results demonstrate that our method outperforms other methods by achieving higher accuracy and making less false segmentation in pancreas extraction.

scholarly journals Liver Segmentation Based on Snakes Model and Improved GrowCut Algorithm in Abdominal CT Image

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Huiyan Jiang ◽  
Baochun He ◽  
Zhiyuan Ma ◽  
Mao Zong ◽  
Xiangrong Zhou ◽  
...  
Keyword(s):  
Ct Images ◽  
Experimental Results ◽  
Initial Contour ◽  
Segmentation Result ◽  
Ct Image ◽  
Liver Segmentation ◽  
Abdominal Ct ◽  
Running Time ◽  
Novel Method ◽  
Liver Region

A novel method based on Snakes Model and GrowCut algorithm is proposed to segment liver region in abdominal CT images. First, according to the traditional GrowCut method, a pretreatment process using K-means algorithm is conducted to reduce the running time. Then, the segmentation result of our improved GrowCut approach is used as an initial contour for the future precise segmentation based on Snakes model. At last, several experiments are carried out to demonstrate the performance of our proposed approach and some comparisons are conducted between the traditional GrowCut algorithm. Experimental results show that the improved approach not only has a better robustness and precision but also is more efficient than the traditional GrowCut method.

scholarly journals Relationship between solitary pulmonary nodule lung cancer and CT image features based on gradual clustering

Open Physics ◽  
2017 ◽  
Vol 15 (1) ◽  
pp. 400-404
Author(s):  
Weipeng Zhang
Keyword(s):  
Lung Cancer ◽  
Pulmonary Nodule ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Image Features ◽  
Ct Image ◽  
Lung Cancers ◽  
Diagnosis Rate ◽  
Malignant Lesions ◽  
The Relationship

Abstract Background The relationship between the medical characteristics of lung cancers and computer tomography (CT) images are explored so as to improve the early diagnosis rate of lung cancers. Methods This research collected CT images of patients with solitary pulmonary nodule lung cancer, and used gradual clustering methodology to classify them. Preliminary classifications were made, followed by continuous modification and iteration to determine the optimal condensation point, until iteration stability was achieved. Reasonable classification results were obtained. Results the clustering results fell into 3 categories. The first type of patients was mostly female, with ages between 50 and 65 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, with pleural indentation; The second type of patients was mostly male with ages between 50 and 80 years. CT images of solitary pulmonary nodule lung cancer for this group contain complete lobulation and burr, but with no pleural indentation; The third type of patients was also mostly male with ages between 50 and 80 years. CT images for this group showed no abnormalities. Conclusions the application of gradual clustering methodology can scientifically classify CT image features of patients with lung cancer in the initial lesion stage. These findings provide the basis for early detection and treatment of malignant lesions in patients with lung cancer.

Tumour delineation and cumulative dose computation in radiotherapy based on deformable registration of respiratory correlated CT images of lung cancer patients

2007 ◽  
Vol 85 (2) ◽  
pp. 232-238 ◽  
Author(s):  
Jonathan Orban de Xivry ◽  
Guillaume Janssens ◽  
Geert Bosmans ◽  
Mathieu De Craene ◽  
André Dekker ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Cumulative Dose ◽  
Ct Images ◽  
Deformable Registration ◽  
Lung Cancer Patients ◽  
Tumour Delineation

scholarly journals AUTOMATED CLASSIFICATION OF SOLITARY PULMONARY NODULES USING CONVOLUTIONAL NEURAL NETWORK BASED ON TRANSFER LEARNING STRATEGY

2021 ◽  
pp. 2140002
Author(s):  
SHIWEI LI ◽  
DANDAN LIU
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Transfer Learning ◽  
Pulmonary Nodule ◽  
Learning Strategy ◽  
Solitary Pulmonary Nodule ◽  
Ct Images ◽  
Ct Image ◽  
Nodule Classification ◽  
Lung Ct

This study aimed to propose an effective malignant solitary pulmonary nodule classification method based on improved Faster R-CNN and transfer learning strategy. In practice, the existing solitary pulmonary nodule classification methods divide the lung cancer images into two categories only: normal and cancerous. This study proposed the deep convolution neural network to classify the computed tomography (CT) images of lung cancer into four categories: lung adenocarcinoma, lung squamous cell carcinoma, metastatic lung cancer, and normal types of lung cancer. Some high-resolution lung CT images have unnecessary characters such as a large number of high-density continuity features, small-size lung nodule targets, CT image background complexity, and so forth. In this study, the CT image sub-block preprocessing strategy was used to extract nodule features for enhancement and alleviate the aforementioned problems. The experimental results showed that the proposed system was effective in resolving issues such as high false-positive rate and long classification time cost based on the original Faster R-CNN detection method. Meanwhile, the transfer learning strategy was used to improve the classification efficiency so as to avoid the overfitting problem caused by a few labeled samples of lung cancer datasets. The classification results were integrated using the majority vote algorithm. The classification results of the lung CT imaging showed that the proposed method had an average detection accuracy of 89.7% and reduced the rate of misdiagnosis to meet the clinical needs.

Sign in / Sign up

Export Citation Format

Share Document