An Automated Lung Nodule Segmentation Method Based On Nodule Detection Network and Region Growing

2019 ◽  
Author(s):  
Yanhao Tan ◽  
Ke Lu ◽  
Jian Xue
Keyword(s):  
Lung Nodule ◽  
Region Growing ◽  
Segmentation Method ◽  
Nodule Detection

scholarly journals A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images

2018 ◽  
Vol 232 ◽  
pp. 02001 ◽  
Author(s):  
Li Zheng ◽  
Yiran Lei
Keyword(s):  
Differential Operator ◽  
Active Contour ◽  
Lung Nodule ◽  
Region Growing ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Computed Tomography Images ◽  
Nodule Detection ◽  
Significant Step ◽  
Low Sensitivity

The detection and segmentation of lung nodules based on computer tomography images (CT) is a basic and significant step to achieve the robotic needle biopsy. In this paper, we reviewed some typical segmentation algorithms, including thresholding, active contour, differential operator, region growing and watershed. To analyse their performance on lung nodule detection, we applied them to four CT images of different kinds of lung nodules. The results show that thresholding, active contour and differential operator do well in the segmentation of solitary nodules, while region growing has an advantage over the others on segmenting nodules adhere to vessels. For segmentation of semi-transparent nodules, differential operator is an especially suitable choice. Watershed can segment nodules adhere to vessels and semi-transparent nodules well, but it has low sensitivity in solitary nodules.

scholarly journals A Context Based Automated System for Lung Nodule Detection in CT Images

2021 ◽  
Author(s):  
Maciej Dajnowiec
Keyword(s):  
Lung Nodule ◽  
Region Growing ◽  
Ct Images ◽  
Automated System ◽  
Data Sets ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Testing Data ◽  
Fully Automatic ◽  
Ct Data

This thesis is focused on automatic lung nodule detection in CT images. CAD systems are suited for this tak because the sheer volume of information present in CT data sets is overwhelming for radiologists to process. The system developed in this thesis presents a fully automatic solution that applies a sequential algoriths which strongly focuses on nodule context. The system operates at a rate of 80% sensitivity with 3.05 FPs per slice. Our testing data, consisting of 19 CTdata sets containing239 lung nodules, is extremely robust when compared with other documented systems. In addition it introduces many new approaches such as a tight bounding, vessel connectivity, perimeter analysis, adaptive MLT and region growing based lung segmentation. The experimental results produced by this systemare an affirmation of the competitiveness of its performance when compared to other documented approaches.

scholarly journals A Context Based Automated System for Lung Nodule Detection in CT Images

2021 ◽  
Author(s):  
Maciej Dajnowiec
Keyword(s):  
Lung Nodule ◽  
Region Growing ◽  
Ct Images ◽  
Automated System ◽  
Data Sets ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Testing Data ◽  
Fully Automatic ◽  
Ct Data

This thesis is focused on automatic lung nodule detection in CT images. CAD systems are suited for this tak because the sheer volume of information present in CT data sets is overwhelming for radiologists to process. The system developed in this thesis presents a fully automatic solution that applies a sequential algoriths which strongly focuses on nodule context. The system operates at a rate of 80% sensitivity with 3.05 FPs per slice. Our testing data, consisting of 19 CTdata sets containing239 lung nodules, is extremely robust when compared with other documented systems. In addition it introduces many new approaches such as a tight bounding, vessel connectivity, perimeter analysis, adaptive MLT and region growing based lung segmentation. The experimental results produced by this systemare an affirmation of the competitiveness of its performance when compared to other documented approaches.

Multi-level 3D Densenets for False-positive Reduction in Lung Nodule Detection Based on Chest Computed Tomography

2020 ◽  
Vol 16 (8) ◽  
pp. 1004-1021
Author(s):  
Xiaoqi Lu ◽  
Yu Gu ◽  
Lidong Yang ◽  
Baohua Zhang ◽  
Ying Zhao ◽  
...  
Keyword(s):  
False Positive ◽  
Contextual Information ◽  
Lung Nodule ◽  
Positive Sample ◽  
Lung Nodules ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Level Information ◽  
Multi Level ◽  
Cade System

Objective: False-positive nodule reduction is a crucial part of a computer-aided detection (CADe) system, which assists radiologists in accurate lung nodule detection. In this research, a novel scheme using multi-level 3D DenseNet framework is proposed to implement false-positive nodule reduction task. Methods: Multi-level 3D DenseNet models were extended to differentiate lung nodules from falsepositive nodules. First, different models were fed with 3D cubes with different sizes for encoding multi-level contextual information to meet the challenges of the large variations of lung nodules. In addition, image rotation and flipping were utilized to upsample positive samples which consisted of a positive sample set. Furthermore, the 3D DenseNets were designed to keep low-level information of nodules, as densely connected structures in DenseNet can reuse features of lung nodules and then boost feature propagation. Finally, the optimal weighted linear combination of all model scores obtained the best classification result in this research. Results: The proposed method was evaluated with LUNA16 dataset which contained 888 thin-slice CT scans. The performance was validated via 10-fold cross-validation. Both the Free-response Receiver Operating Characteristic (FROC) curve and the Competition Performance Metric (CPM) score show that the proposed scheme can achieve a satisfactory detection performance in the falsepositive reduction track of the LUNA16 challenge. Conclusion: The result shows that the proposed scheme can be significant for false-positive nodule reduction task.

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