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%.

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.

scholarly journals Two-Stage Liver and Tumor Segmentation Algorithm Based on Convolutional Neural Network

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1806
Author(s):  
Lu Meng ◽  
Qianqian Zhang ◽  
Sihang Bu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Liver Tumors ◽  
Segmentation Algorithm ◽  
Tumor Segmentation ◽  
Liver Segmentation ◽  
Dice Coefficient ◽  
Long Distance ◽  
Two Stage ◽  
Public Data

The liver is an essential metabolic organ of the human body, and malignant liver tumors seriously affect and threaten human life. The segmentation algorithm for liver and liver tumors is one of the essential branches of computer-aided diagnosis. This paper proposed a two-stage liver and tumor segmentation algorithm based on the convolutional neural network (CNN). In the present study, we used two stages to segment the liver and tumors: liver localization and tumor segmentation. In the liver localization stage, the network segments the liver region, adopts the encoding–decoding structure and long-distance feature fusion operation, and utilizes the shallow features’ spatial information to improve liver identification. In the tumor segmentation stage, based on the liver segmentation results of the first two steps, a CNN model was designed to accurately identify the liver tumors by using the 2D image features and 3D spatial features of the CT image slices. At the same time, we use the attention mechanism to improve the segmentation performance of small liver tumors. The proposed algorithm was tested on the public data set Liver Tumor Segmentation Challenge (LiTS). The Dice coefficient of liver segmentation was 0.967, and the Dice coefficient of tumor segmentation was 0.725. The proposed algorithm can accurately segment the liver and liver tumors in CT images. Compared with other state-of-the-art algorithms, the segmentation results of the proposed algorithm rank the highest in the Dice coefficient.

DIAGNOSIS OF LUNG CANCER USING MULTISCALE CONVOLUTIONAL NEURAL NETWORK

2020 ◽  
Vol 32 (05) ◽  
pp. 2050030
Author(s):  
Homayoon Yektaei ◽  
Mohammad Manthouri
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Deep Convolutional Neural Networks ◽  
Human Errors ◽  
Classical State ◽  
Entire Lung ◽  
Artificial Neural Network Ann ◽  
Diagnosis Of Lung Cancer

Lung cancer is one of the dangerous diseases that cause huge cancer death worldwide. Early detection of lung cancer is the only possible way to improve a patient’s chance for survival. This study presents an innovative automated diagnosis classification method for Computed Tomography (CT) images of lungs. In this paper, the CT scan of lung images was analyzed with the multiscale convolution. The entire lung is segmented from the CT images and the parameters are calculated from the segmented image. The use of image processing techniques and identifying patterns in the detection of lung cancer from CT images reduces human errors in detecting tumors, and speeds up diagnosis time. Artificial Neural Network (ANN) has been widely used to detect lung cancer, and has significantly reduced the percentage of errors. Therefore, in this paper, Convolution Neural Network (CNN), which is the most effective method, is used for the detection of various types of cancers. This study presents a Multiscale Convolutional Neural Network (MCNN) approach for the classification of tumors. Based on the structure of MCNN, which presents CT picture to several deep convolutional neural networks with different size and resolutions, the classical handcrafted features extraction step is avoided. The proposed approach gives better classification rates than the classical state of the art methods allowing a safer Computer-Aided Diagnosis of pleural cancer. This study reaches a diagnosis accuracy of [Formula: see text] using multiscale convolution technique, which reveals the efficiency of the proposed method.

scholarly journals Classification of Lung Nodules using Improved Residual Convolutional Neural Network

2020 ◽  
Vol 1 (1) ◽  
pp. 15-21
Author(s):  
Salah Eldeen Babiker ◽  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Early Stage ◽  
Lung Nodule ◽  
Early Stage Lung Cancer ◽  
Evolutionary Neural Network ◽  
Diagnosis Of Lung Cancer ◽  
Experimental Findings ◽  
Lung Cancer Detection

The most common cancer of the lung cannot be ignored and can cause late-health death. Now CT can be used to help clinicians diagnose early-stage lung cancer. In certain cases the diagnosis of lung cancer detection is based on doctors' intuition, which can neglect other patients and cause complications. Deep learning in most other areas of medical diagnosis has proven to be a common and powerful tool. This research is planned for improving the residual evolutionary neural network (IRCNN). These networks apply with some changes to the benign and malignant lung nodule to the CT image classification task. The segmenting of the nodule is performed here by clustering k-means. The LIDC-IDRI database analysed those networks. Experimental findings show that the IRCNN network archived the best performance of lung nodule classification, which findings best among established methods.

scholarly journals Diagnosis of Lung Cancer Disease Based on Back-Propagation Artificial Neural Network Algorithm

2020 ◽  
Vol 38 (3B) ◽  
pp. 184-196
Author(s):  
Hanan A. R. Akkar ◽  
Suhad Q. Hadad
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Early Stage ◽  
Back Propagation ◽  
Graphic User Interface ◽  
Cancer Disease ◽  
Intelligent Technique ◽  
Artificial Neural ◽  
Diagnosis Of Lung Cancer

Early stage detection of lung cancer is important for successful controlling of the diseases, also to offer additional chance to the patients in order to survive. So , algorithms that are related with computer vision and Image processing are extremely important for early medical diagnosis of lung cancer. In current work () computed tomography scan images were collected from several patients Classification was done using Back Propagation Artificial Neural Network ( ).It is considered as a powerful artificially intelligent technique with training rule for optimization to update the weights of the overall connections in order to determine the abnormal image. Several pre-processing operations and morphologic techniques were introduced to improve the condition of the image and make it suitable for detection cancer.Histogram and () Gray Level Co-occurrence Matrix were applied toget best features extraction analysis from lung image.Three types of activation functions(trainlm ,trainbr ,traingd) were used which gives a significant accuracy for detecting cancer in  scan lung image related to the suggested algorithm. Best results were obtained with accuracy rate 95.9 % in trainlm activation function.. Graphic User Interface ( ) was displaying to show the final diagnosis  for lung.

scholarly journals FlowCell-enriched circulating tumor cells as a predictor of lung cancer metastasis

Human Cell ◽  
2021 ◽  
Author(s):  
Yan Lu ◽  
Yushuang Zheng ◽  
Yuhong Wang ◽  
Dongmei Gu ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Tumor Cells ◽  
Circulating Tumor Cells ◽  
Cancer Metastasis ◽  
Lung Tumor ◽  
Early Stage ◽  
High Rate ◽  
Early Stage Lung Cancer ◽  
Lung Cancer Metastasis ◽  
Number Of Patients

AbstractLung cancer is the most fetal malignancy due to the high rate of metastasis and recurrence after treatment. A considerable number of patients with early-stage lung cancer relapse due to overlooked distant metastasis. Circulating tumor cells (CTCs) are tumor cells in blood circulation that originated from primary or metastatic sites, and it has been shown that CTCs are critical for metastasis and prognosis in various type of cancers. Here, we employed novel method to capture, isolate and classify CTC with FlowCell system and analyzed the CTCs from a cohort of 302 individuals. Our results illustrated that FlowCell-enriched CTCs effectively differentiated benign and malignant lung tumor and the total CTC counts increased as the tumor developed. More importantly, we showed that CTCs displayed superior sensitivity and specificity to predict lung cancer metastasis in comparison to conventional circulating biomarkers. Taken together, our data suggested CTCs can be used to assist the diagnosis of lung cancer as well as predict lung cancer metastasis. These findings provide an alternative means to screen early-stage metastasis.

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