scholarly journals Water Cycle Bat Algorithm and Dictionary-Based Deformable Model for Lung Tumor Segmentation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mamtha V. Shetty ◽  
D. Jayadevappa ◽  
G. N. Veena
Keyword(s):  
Lung Cancer ◽  
Water Cycle ◽  
Lung Tumor ◽  
Median Filter ◽  
Bat Algorithm ◽  
Input Image ◽  
Deformable Model ◽  
Tumor Segmentation ◽  
Lung Lobe ◽  
Bayesian Fuzzy Clustering

Among the different types of cancers, lung cancer is one of the widespread diseases which causes the highest number of deaths every year. The early detection of lung cancer is very essential for increasing the survival rate in patients. Although computed tomography (CT) is the preferred choice for lungs imaging, sometimes CT images may produce less tumor visibility regions and unconstructive rates in tumor portions. Hence, the development of an efficient segmentation technique is necessary. In this paper, water cycle bat algorithm- (WCBA-) based deformable model approach is proposed for lung tumor segmentation. In the preprocessing stage, a median filter is used to remove the noise from the input image and to segment the lung lobe regions, and Bayesian fuzzy clustering is applied. In the proposed method, deformable model is modified by the dictionary-based algorithm to segment the lung tumor accurately. In the dictionary-based algorithm, the update equation is modified by the proposed WCBA and is designed by integrating water cycle algorithm (WCA) and bat algorithm (BA).

Automatic segmentation of lung tumors on CT images based on a 2D & 3D hybrid convolutional neural network

2021 ◽  
pp. 20210038
Author(s):  
Wutian Gan ◽  
Hao Wang ◽  
Hengle Gu ◽  
Yanhua Duan ◽  
Yan Shao ◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Lung Tumor ◽  
Ct Images ◽  
Hybrid Network ◽  
Evaluation Metrics ◽  
Tumor Segmentation ◽  
Tumor Delineation ◽  
3D Cnn ◽  
The Individual

Objective: A stable and accurate automatic tumor delineation method has been developed to facilitate the intelligent design of lung cancer radiotherapy process. The purpose of this paper is to introduce an automatic tumor segmentation network for lung cancer on CT images based on deep learning. Methods: In this paper, a hybrid convolution neural network (CNN) combining 2D CNN and 3D CNN was implemented for the automatic lung tumor delineation using CT images. 3D CNN used V-Net model for the extraction of tumor context information from CT sequence images. 2D CNN used an encoder–decoder structure based on dense connection scheme, which could expand information flow and promote feature propagation. Next, 2D features and 3D features were fused through a hybrid module. Meanwhile, the hybrid CNN was compared with the individual 3D CNN and 2D CNN, and three evaluation metrics, Dice, Jaccard and Hausdorff distance (HD), were used for quantitative evaluation. The relationship between the segmentation performance of hybrid network and the GTV volume size was also explored. Results: The newly introduced hybrid CNN was trained and tested on a dataset of 260 cases, and could achieve a median value of 0.73, with mean and stand deviation of 0.72 ± 0.10 for the Dice metric, 0.58 ± 0.13 and 21.73 ± 13.30 mm for the Jaccard and HD metrics, respectively. The hybrid network significantly outperformed the individual 3D CNN and 2D CNN in the three examined evaluation metrics (p < 0.001). A larger GTV present a higher value for the Dice metric, but its delineation at the tumor boundary is unstable. Conclusions: The implemented hybrid CNN was able to achieve good lung tumor segmentation performance on CT images. Advances in knowledge: The hybrid CNN has valuable prospect with the ability to segment lung tumor.

A Novel Approach of Lung Tumor Segmentation Using a 3D Deep Convolutional Neural Network

2022 ◽  
pp. 1-16
Author(s):  
Shweta Tyagi ◽  
Sanjay N. Talbar ◽  
Abhishek Mahajan
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Convolutional Neural Network ◽  
Lung Tumor ◽  
Early Stage ◽  
Tumor Segmentation ◽  
Lung Tumour ◽  
Dice Coefficient ◽  
Tumour Segmentation ◽  
Diagnosis Of Lung Cancer

Cancer is one of the most life-threatening diseases in the world, and lung cancer is the leading cause of death worldwide. If not detected at an early stage, the survival rate of lung cancer patients can be very low. To treat patients in later stages, one needs to analyze the tumour region. For accurate diagnosis of lung cancer, the first step is to detect and segment the tumor. In this chapter, an approach for segmentation of a lung tumour is presented. For pre-processing of lung CT images, simple image processing like morphological operations is used, and for tumour segmentation task, a 3D convolutional neural network (CNN) is used. The CNN architecture consists of a 3D encoder block followed by 3D decoder block just like U-Net but with deformable convolution blocks. For this study, two datasets have been used; one is the online-available NSCLC Radiomics dataset, and the other is collected from an Indian local hospital. The approach proposed in this chapter is evaluated in terms of dice coefficient. This approach is able to give significant results with a dice coefficient of 77.23%.

scholarly journals Segmentation of Lung Cancer using Deep learning

2019 ◽  
Vol 8 (2) ◽  
pp. 1188-1192
Keyword(s):  
Lung Cancer ◽  
Image Segmentation ◽  
Lung Tumor ◽  
Public Health Problem ◽  
Image Database ◽  
Advanced Technology ◽  
Tumor Segmentation ◽  
Major Public Health Problem ◽  
Magnetic Resonance Imaging Mri ◽  
Time Required

Lung cancer is one among the deadliest and dangerous widespread diseases that create a major public health problem. The main aim of this paper is to basically segment the image or to identify the nodule present in the image and provide the accuracy of that segmented image. In this concern, proper segmentation of lung tumor present in the X-ray scans or Magnetic Resonance Imaging (MRI) or Computed Tomography (CT scan) is the first stone towards achieving completely automated diagnosis system for lung cancer detection of the patient. With the advanced technology and availability of dataset, the time required for a radiologist can be saved using CAD tools for tumor segmentation. In this work, we use an approach called data driven for lung tumor segmentation from CT scans by using UNet . In our approach we will train the network by using CT image with tumor having the slices of size (512 × 512 × 1). Our model has been trained and tested on the LUNA16 dataset considering 10 patients, provided by or used by Lung image database consortium (LIDC) and the image database resources initiative (IDRI). In this dataset, our proposed technique will achieve an average dice score of 0.8507. This can further be analyzed or used for other medical images to find the nodule or with other applications such as in brain image segmentation and liver image segmentation.

A Hybrid deep learning model for effective segmentation and classification of lung nodules from CT images

2021 ◽  
pp. 1-13
Author(s):  
Malathi Murugesan ◽  
Kalaiselvi Kaliannan ◽  
Shankarlal Balraj ◽  
Kokila Singaram ◽  
Thenmalar Kaliannan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Learning ◽  
Lung Tumor ◽  
Median Filter ◽  
Lung Nodule ◽  
Rapid Identification ◽  
Lung Nodules ◽  
Classification Models ◽  
Deep Learning Model

Deep learning algorithms will be used to detect lung nodule anomalies at an earlier stage. The primary goal of this effort is to properly identify lung cancer, which is critical in preserving a person’s life. Lung cancer has been a source of concern for people all around the world for decades. Several researchers presented numerous issues and solutions for various stages of a computer-aided system for diagnosing lung cancer in its early stages, as well as information about lung cancer. Computer vision is one of the field of artificial intelligence this is a better way to detect and prevent the lung cancer. This study focuses on the stages involved in detecting lung tumor regions, namely pre-processing, segmentation, and classification models. An adaptive median filter is used in pre-processing to identify the noise. The work’s originality seeks to create a simple yet effective model for the rapid identification and U-net architecture based segmentation of lung nodules. This approach focuses on the identification and segmentation of lung cancer by detecting picture normalcy and abnormalities.

scholarly journals Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0205003 ◽  
Author(s):  
Constance A. Owens ◽  
Christine B. Peterson ◽  
Chad Tang ◽  
Eugene J. Koay ◽  
Wen Yu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Lung Tumor ◽  
Small Cell ◽  
Tumor Segmentation ◽  
Small Cell Lung ◽  
Segmentation Methods

Lower lobe origin is related to unfavorable outcomes in patients with stage I–III lung cancer treated with radical chemoradiotherapy

2020 ◽  
pp. 030089162097135
Author(s):  
Fangfang Liu ◽  
Xun Yuan ◽  
Jizong Jiang ◽  
Peng Zhang ◽  
Yuan Chen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Lung Tumor ◽  
Survival Rates ◽  
Lower Lobe ◽  
Tumor Location ◽  
Stage I ◽  
Lung Lobe ◽  
Tumor Origin ◽  
Lower Lung ◽  
The Difference

Purpose: The influence of tumor location on survival was investigated in patients with lung cancer who received radical chemoradiotherapy. Methods: We examined the relationships between radiation site and survival outcome in patients with lung cancer. A total of 14,640 patients with lung cancer who received radical chemoradiotherapy for stage I–III disease were reviewed from Surveillance, Epidemiology, and End Results Program (SEER) datasets. We further retrospectively collected cases from a cohort of 148 eligible patients diagnosed between December 2013 and December 2019. Results: Female sex, adenocarcinoma, and stage III disease were significantly correlated with right lung lobe tumor. Advanced age at diagnosis was associated with lower lung tumor origin. For the patients who received radical chemoradiotherapy, 1- and 3-year survival rates were 56.5% and 22.9%. Lower lobe origin was closely related to a shorter overall survival compared to non-right lower lobe tumors ( p < 0.001). We also validated the difference in our cohort ( p = 0.004). Conclusions: Our results suggest that lower lobe tumor increases mortality risk in patients with lung cancer treated with radical chemoradiotherapy.

scholarly journals Automatic Lung Tumor Segmentation on PET/CT Images Using Fuzzy Markov Random Field Model

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Yu Guo ◽  
Yuanming Feng ◽  
Jian Sun ◽  
Ning Zhang ◽  
Wang Lin ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Random Field ◽  
Markov Random Field ◽  
Lung Tumor ◽  
Ct Images ◽  
Tumor Segmentation ◽  
Posterior Probability Distribution ◽  
Markov Random ◽  
Positron Emission ◽  
Pet Ct

The combination of positron emission tomography (PET) and CT images provides complementary functional and anatomical information of human tissues and it has been used for better tumor volume definition of lung cancer. This paper proposed a robust method for automatic lung tumor segmentation on PET/CT images. The new method is based on fuzzy Markov random field (MRF) model. The combination of PET and CT image information is achieved by using a proper joint posterior probability distribution of observed features in the fuzzy MRF model which performs better than the commonly used Gaussian joint distribution. In this study, the PET and CT simulation images of 7 non-small cell lung cancer (NSCLC) patients were used to evaluate the proposed method. Tumor segmentations with the proposed method and manual method by an experienced radiation oncologist on the fused images were performed, respectively. Segmentation results obtained with the two methods were similar and Dice’s similarity coefficient (DSC) was 0.85 ± 0.013. It has been shown that effective and automatic segmentations can be achieved with this method for lung tumors which locate near other organs with similar intensities in PET and CT images, such as when the tumors extend into chest wall or mediastinum.

scholarly journals Recognition of Lung Tumor Area Based on Watershed Segmentation and CNN

2020 ◽  
Vol 9 (8) ◽  
pp. 20-24
Keyword(s):  
Ct Scan ◽  
Lung Tumor ◽  
Early Recognition ◽  
Early Stage ◽  
Median Filter ◽  
Input Image ◽  
Watershed Segmentation ◽  
Complex Wavelet Transform ◽  
Occurrence Matrix ◽  
Preprocessing Technique

Early recognition of tumor would assist in saving an enormous number of lives over the globe frequently. Study and remedy of lung tumor have been one of the greatest troubles faced by humans over the latest few decades. Effective recognition of lung tumor is a vital and crucial aspect of image processing. Several Segmentation methods were used to detect lung tumor at an early stage. An approach is presented in this paper to diagnose lung tumor from CT scan images. The input image (CT scan image) will be preprocessed initially using median filter to remove the noise. After applying preprocessing technique, the Dual-Tree Complex Wavelet Transform (DTCWT) segmentation technique is used for the edge detection. The Gray-Level Co-occurrence Matrix (GLCM) features are calculated based on the pixel values of the extracted image. These features can be compared with database images using Convolutional Neural Network (CNN) which facilitates in categorizing it as tumorous. After confirming that the affected area is tumorous, watershed segmentation algorithm is used to get the color features of the tumor.

Improved Therapeutic Efficacy of Topotecan Against A549 Lung Cancer Cells with Folate-targeted Topotecan Liposomes

2020 ◽  
Vol 21 (11) ◽  
pp. 902-909
Author(s):  
Jingxin Zhang ◽  
Weiyue Shi ◽  
Gangqiang Xue ◽  
Qiang Ma ◽  
Haixin Cui ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Drug Delivery ◽  
Cancer Cells ◽  
Targeted Delivery ◽  
Therapeutic Efficacy ◽  
Drug Delivery Systems ◽  
Lung Tumor ◽  
A549 Cells ◽  
Delivery Systems ◽  
Lung Cancer Cells

Background: Among all cancers, lung cancer has high mortality among patients in most of the countries in the world. Targeted delivery of anticancer drugs can significantly reduce the side effects and dramatically improve the effects of the treatment. Folate, a suitable ligand, can be modified to the surface of tumor-selective drug delivery systems because it can selectively bind to the folate receptor, which is highly expressed on the surface of lung tumor cells. Objective: This study aimed to construct a kind of folate-targeted topotecan liposomes for investigating their efficacy and mechanism of action in the treatment of lung cancer in preclinical models. Methods: We conjugated topotecan liposomes with folate, and the liposomes were characterized by particle size, entrapment efficiency, cytotoxicity to A549 cells and in vitro release profile. Technical evaluations were performed on lung cancer A549 cells and xenografted A549 cancer cells in female nude mice, and the pharmacokinetics of the drug were evaluated in female SD rats. Results: The folate-targeted topotecan liposomes were proven to show effectiveness in targeting lung tumors. The anti-tumor effects of these liposomes were demonstrated by the decreased tumor volume and improved therapeutic efficacy. The folate-targeted topotecan liposomes also lengthened the topotecan blood circulation time. Conclusion: The folate-targeted topotecan liposomes are effective drug delivery systems and can be easily modified with folate, enabling the targeted liposomes to deliver topotecan to lung cancer cells and kill them, which could be used as potential carriers for lung chemotherapy.

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