3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS)

Farhangi, M. Mehdi; Frigui, Hichem; Seow, Albert; Amini, Amir A.

doi:10.1109/tmi.2017.2720119

Cited by 34 publications

(15 citation statements)

References 30 publications

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“…Each scan was annotated by four experienced chest radiologists. A total of 2632 nodules are included in the LIDC dataset, with the largest nodules not exceeding 64 × 64 pixels in section, referring to 29,30 . There is an associated XML file that indicates the locations of nodules on each 512 × 512 slice.…”

Section: Methodsmentioning

confidence: 99%

A serialized classification method for pulmonary nodules based on lightweight cascaded convolutional neural network‐long short‐term memory

Peng

2020

Int J Imaging Syst Tech

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Computer Assisted Diagnosis (CAD) is an effective method to detect lung cancer from computed tomography (CT) scans. The development of artificial neural network makes CAD more accurate in detecting pathological changes. Due to the complexity of the lung environment, the existing neural network training still requires large datasets, excessive time, and memory space. To meet the challenge, we analysis 3D volumes as serialized 2D slices and present a new neural network structure lightweight convolutional neural network (CNN)‐long short‐term memory (LSTM) for lung nodule classification. Our network contains two main components: (a) optimized lightweight CNN layers with tiny parameter space for extracting visual features of serialized 2D images, and (b) LSTM network for learning relevant information among 2D images. In all experiments, we compared the training results of several models and our model achieved an accuracy of 91.78% for lung nodule classification with an AUC of 93%. We used fewer samples and memory space to train the model, and we achieved faster convergence. Finally, we analyzed and discussed the feasibility of migrating this framework to mobile devices. The framework can also be applied to cope with the small amount of training data and the development of mobile health device in future.

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Section: Methodsmentioning

confidence: 99%

A serialized classification method for pulmonary nodules based on lightweight cascaded convolutional neural network‐long short‐term memory

Peng

2020

Int J Imaging Syst Tech

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“…There is ability to converge to the specific boundary in minimum iterations. Farhangi et al [4] stated that the model preferred the prior shape for sparse linear combinations of training data. In this method, they used Chan-Vese algorithm for region based contour model for curve evolution technique used for their design.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model

Gopi¹,

Selvakumar²

2018

IJET

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Lung cancer is the most common leading cancer in both men and women all over the world. Accurate image segmentation is an essential image analysis tool that is responsible for partitioning an image into several sub-regions. Active contour model have been widely used for effective image segmentation methods as this model produce sub-regions with continuous boundaries. It is used in the applications such as image analysis, deep learning, computer vision and machine learning. Advanced level set method helps to implement active contours for image segmentation with good boundary detection accuracy. This paper proposes a model based on active contour using level set methods for segmentation of such lung CT images and focusing 3D lesion refinement. The features were determined by applying a multi-scale Gaussian filter. This proposed method is able to detect lung tumors in CT images with intensity, homogeneity and noise. The proposed method uses LIDC-IDRI dataset images to segment accurate 3D lesion of lung tumor CT images.

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“…Previously, authors have proposed various approaches to segment lung nodules from CT scans. In summary, these methods can be categorized into four general approaches: thresholding methods, region growing, active contours, and graph‐cut methods . In early attempts, Reeves et al performed segmentation by applying an adaptive threshold computed for each scan to compensate for the variations between two consecutive scans in conjunction with a geometrical constraint to favor spherical shape structures.…”

Section: Introductionmentioning

confidence: 99%

“…Three‐dimensional segmentation was carried out by two‐dimensional (2D) active contours slice by slice, subsequently stacking the resulting planes on top of each other to form the 3D result. In another study in this domain, a sparse representation of training shapes were incorporated into active contours to contribute adaptively to the conventional active contour energy function. Graph‐cuts segmentation, a method adopted in this paper, is another approach to segment nodules in the lung.…”

Section: Introductionmentioning

confidence: 99%

Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT

2017

Self Cite

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Purpose: We have developed a robust tool for performing volumetric and temporal analysis of nodules from respiratory gated four-dimensional (4D) CT. The method could prove useful in IMRT of lung cancer. Methods: We modified the conventional graph-cuts method by adding an adaptive shape prior as well as motion information within a signed distance function representation to permit more accurate and automated segmentation and tracking of lung nodules in 4D CT data. Active shape models (ASM) with signed distance function were used to capture the shape prior information, preventing unwanted surrounding tissues from becoming part of the segmented object. The optical flow method was used to estimate the local motion and to extend three-dimensional (3D) segmentation to 4D by warping a prior shape model through time. The algorithm has been applied to segmentation of wellcircumscribed, vascularized, and juxtapleural lung nodules from respiratory gated CT data. Results: In all cases, 4D segmentation and tracking for five phases of high-resolution CT data took approximately 10 min on a PC workstation with AMD Phenom II and 32 GB of memory. The method was trained based on 500 breath-held 3D CT data from the LIDC data base 1 and was tested on 17 4D lung nodule CT datasets consisting of 85 volumetric frames. The validation tests resulted in an average Dice Similarity Coefficient (DSC) = 0.68 for all test data. An important by-product of the method is quantitative volume measurement from 4D CT from end-inspiration to end-expiration which will also have important diagnostic value. Conclusion: The algorithm performs robust segmentation of lung nodules from 4D CT data. Signed distance ASM provides the shape prior information which based on the iterative graph-cuts framework is adaptively refined to best fit the input data, preventing unwanted surrounding tissue from merging with the segmented object.

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3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS)

Cited by 34 publications

References 30 publications

A serialized classification method for pulmonary nodules based on lightweight cascaded convolutional neural network‐long short‐term memory

A serialized classification method for pulmonary nodules based on lightweight cascaded convolutional neural network‐long short‐term memory

Analysis of Lung Tumour Detection and Segmentation Using Level Set Method of Active Contour Model

Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT

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