Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor

Choi, Wookjin; Choi, Tae‐Sun

doi:10.1016/j.cmpb.2013.08.015

Cited by 174 publications

(93 citation statements)

References 39 publications

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“…Some techniques have achieved sensitivity greater than 95%, such as the ones proposed by Choi and Choi [24], Badura and Pietka [18], Wang et al [25], Cascio et al [26], Chen et al [27], Suiyuan and Junfeng [29], Ozekes and Osman [37], Ozekes, Osman and Ucan [38]. However, most of the techniques that had a sensitivity greater than 95% had a high rate of false positives, and other issues that need a detailed analysis.…”

Section: Discussionmentioning

confidence: 99%

“…After this analysis, only 38 articles [24,4,18,22,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58] were identified as 3D automated algorithms to segment lung nodules in CT images, the target of this work.…”

Section: Work Selection Criteriamentioning

confidence: 99%

“…The radiologist may also suffer interference factors such as fatigue, Authors Computational technique(s) Choi and Choi [24], Santos et al [4], Chen et al [27] and Li and Doi [80] Hessian matrix based method El-Baz et al [22] and Le et al [81] Genetic algorithm template matching Cascio et al [26] Stable 3D mass-spring models Soltaninejad, Keshani and Tajeripour [28] k-Nearest Neighbors (k-NN) classifier and active contour Suiyuan and Junfeng [29] Thresholding Awai et al [82] Sieve filter Tanino et al [83] Variable n-quoit filter Riccardi et al [30] 3D fast radial transform Namin et al [32] and Murphy et al [84] Shape index Ozekes, Osman and Ucan [38] 3D template matching Ge et al [45] Adaptive weighted k-means clustering Yamada et al [85] and Kanazawa et al [86] Fuzzy clustering Mekada et al [51] Maximum distance inside a connected component Mao et al [87] Fragmentary window filtering Mendonça et al [88] Curvature tensor Paik et al [89] Statistical shape model Agam and Armato [90] Correlation-based enhancement filters Wang et al [25] and Armato III et al [91] Multiple gray-level thresholding Saita et al [92] 3D labeling method subjectivity of the analysis, images acquired with improper configuration of the equipment and noise. A detailed analysis of the LIDC-IDRI database can help us understand the difficulties encountered during this task.…”

Section: False Positive Reductionmentioning

confidence: 99%

“…Computational classifier Santos et al [4], Wang et al [25], Choi and Choi [24], Riccardi et al [30], Liu et al [31], Ozekes and Osman [37], Yang, Periaswamy and Wu [39] and Orozco et al [94] Support vector machines (SVM) El-Baz et al [22] Bayesian supervised Cascio et al [26], Ashwin et al [72], Lin et al [95] and Bellotti et al [96] Artificial neural networks (ANN)…”

Section: Authorsmentioning

confidence: 99%

“…Choi and Choi [24] developed an automatic technique for the detection of lung nodules based on a feature descriptor guided by the 3D shape of the object. In this technique, after the lung nodule segmentation, potential nodules are detected using multi-scale dot enhancement filtering, followed by extracting features of each nodule and their refinement using the eliminating edges technique.…”

Section: Selected Workmentioning

confidence: 99%

See 4 more Smart Citations

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

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This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images.Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic * Corresponding author. E-mail: tavares@fe.up.pt. 31, 2015 aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks. Preprint submitted to Computer Methods and Programs in Biomedicine August

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Section: Discussionmentioning

confidence: 99%

Section: Work Selection Criteriamentioning

confidence: 99%

Section: False Positive Reductionmentioning

confidence: 99%

Section: Authorsmentioning

confidence: 99%

Section: Selected Workmentioning

confidence: 99%

See 3 more Smart Citations

Automatic 3D pulmonary nodule detection in CT images: A survey

Valente

Cortez

Neto

et al. 2016

Computer Methods and Programs in Biomedicine

202

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Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Saad

Lee

Choi

2019

Int J Imaging Syst Tech

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Quantitative reasoning in medical decision science relies on the delineation of pathological objects. For example, evidence‐based clinical decisions regarding lung diseases require the segmentation of nodules, tumors, or cancers. Non‐small cell lung cancer (NSCLC) tends to be large sized, irregularly shaped, and grows against surrounding structures imposing challenges in the segmentation, even for expert clinicians. An automated delineation tool based on spatial analysis was developed and studied on 25 sets of computed tomography scans of NSCLC. Manual and automated delineations were compared, and the proposed method exhibited robustness in terms of the tumor size (5.32–18.24 mm), shape (spherical or irregular), contouring (lobulated, spiculated, or cavitated), localization (solitary, pleural, mediastinal, endobronchial, or tagging), and laterality (left or right lobe) with accuracy between 80% and 99%. Small discrepancies observed between the manual and automated delineations may arise from the variability in the practitioners' definitions of region of interest or imaging artifacts that reduced the tissue resolution.

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Modeling an effectual multi‐section You Only Look Once for enhancing lung cancer prediction

Jena

George

Ponraj

2021

Int J Imaging Syst Tech

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The shape and size of nodules are significant pointers of malignancy in cancer diagnosis. Moreover, considerable capture of nodules' structural data attained from computed tomography (CT) scans in computer-aided design is a confronting task. Various investigations deal with computationally deep ensemble approaches/convolutional neural network (CNN) models; however, sampling tumors based on multi-section-You Only Look Once (MS-YOLO) architecture, this anticipated model acquires nodules' multi-sections from various views and encodes nodule's information to data aggregation from diverse cross-sections through pooling layers. Subsequently, the features are utilized for the nodule classification task. This MS YOLO does not need any nodules' spatial annotation. However, it works directly over the cross-section acquired from enclosed nodule volume. This work has been analyzed using LUNA R16 dataset. It attains adequate performance with a mean value of 90.8% classification accuracy. Anticipated architecture is cast-off to choose cross-section determination of malignancy that helps in output interpretation. The proposed model shows better trade-off among previous methodologies. Simulation was carried out in MATLAB environment.

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Automated pulmonary nodule detection based on three-dimensional shape-based feature descriptor

Cited by 174 publications

References 39 publications

Automatic 3D pulmonary nodule detection in CT images: A survey

Automatic 3D pulmonary nodule detection in CT images: A survey

Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science

Modeling an effectual multi‐section You Only Look Once for enhancing lung cancer prediction

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