Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification
In computed tomography, automated detection of pulmonary nodules with a broad spectrum of appearance is still a challenge, especially, in the detection of small nodules. An automated detection system usually contains two major steps: candidate detection and false positive (FP) reduction. We propose...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8676287/ |
| _version_ | 1818924372308525056 |
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| author | Jun Wang Jiawei Wang Yaofeng Wen Hongbing Lu Tianye Niu Jiangfeng Pan Dahong Qian |
| author_facet | Jun Wang Jiawei Wang Yaofeng Wen Hongbing Lu Tianye Niu Jiangfeng Pan Dahong Qian |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | In computed tomography, automated detection of pulmonary nodules with a broad spectrum of appearance is still a challenge, especially, in the detection of small nodules. An automated detection system usually contains two major steps: candidate detection and false positive (FP) reduction. We propose a novel strategy for fast candidate detection from volumetric chest CT scans, which can minimize false negatives (FNs) and false positives (FPs). The core of the strategy is a nodule-size-adaptive deep model that can detect nodules of various types, locations, and sizes from 3D images. After candidate detection, each result is located with a bounding cube, which can provide rough size information of the detected objects. Furthermore, we propose a simple yet effective CNNs-based classifier for FP reduction, which benefits from the candidate detection. The performance of the proposed nodule detection was evaluated on both independent and publicly available datasets. Our detection could reach high sensitivity with few FPs and it was comparable with the state-of-the-art systems and manual screenings. The study demonstrated that excellent candidate detection plays an important role in the nodule detection and can simplify the design of the FP reduction. The proposed candidate detection is an independent module, so it can be incorporated with any other FP reduction methods. Besides, it can be used as a potential solution for other similar clinical applications. |
| first_indexed | 2024-12-20T02:24:17Z |
| format | Article |
| id | doaj.art-13a64912430548358d42cef6f9180bfc |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T02:24:17Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-13a64912430548358d42cef6f9180bfc2022-12-21T19:56:45ZengIEEEIEEE Access2169-35362019-01-017460334604410.1109/ACCESS.2019.29081958676287Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and ClassificationJun Wang0https://orcid.org/0000-0001-9115-3755Jiawei Wang1Yaofeng Wen2Hongbing Lu3Tianye Niu4https://orcid.org/0000-0003-4181-3641Jiangfeng Pan5Dahong Qian6Institute of Translational Medicine, Zhejiang University, Hangzhou, ChinaDepartment of Radiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, ChinaShanghai Industrial Technology Institute, Shanghai, ChinaCollege of Computer Science and Technology, Zhejiang University, Hangzhou, ChinaInstitute of Translational Medicine, Zhejiang University, Hangzhou, ChinaMedical Imaging Department, Jinhua Municipal Central Hospital, Jinhua, ChinaDeepwise Healthcare Joint Research Laboratory, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, ChinaIn computed tomography, automated detection of pulmonary nodules with a broad spectrum of appearance is still a challenge, especially, in the detection of small nodules. An automated detection system usually contains two major steps: candidate detection and false positive (FP) reduction. We propose a novel strategy for fast candidate detection from volumetric chest CT scans, which can minimize false negatives (FNs) and false positives (FPs). The core of the strategy is a nodule-size-adaptive deep model that can detect nodules of various types, locations, and sizes from 3D images. After candidate detection, each result is located with a bounding cube, which can provide rough size information of the detected objects. Furthermore, we propose a simple yet effective CNNs-based classifier for FP reduction, which benefits from the candidate detection. The performance of the proposed nodule detection was evaluated on both independent and publicly available datasets. Our detection could reach high sensitivity with few FPs and it was comparable with the state-of-the-art systems and manual screenings. The study demonstrated that excellent candidate detection plays an important role in the nodule detection and can simplify the design of the FP reduction. The proposed candidate detection is an independent module, so it can be incorporated with any other FP reduction methods. Besides, it can be used as a potential solution for other similar clinical applications.https://ieeexplore.ieee.org/document/8676287/Computed tomographypulmonary noduleobject detectiondeep-learningconvolutional neural networks |
| spellingShingle | Jun Wang Jiawei Wang Yaofeng Wen Hongbing Lu Tianye Niu Jiangfeng Pan Dahong Qian Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification IEEE Access Computed tomography pulmonary nodule object detection deep-learning convolutional neural networks |
| title | Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification |
| title_full | Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification |
| title_fullStr | Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification |
| title_full_unstemmed | Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification |
| title_short | Pulmonary Nodule Detection in Volumetric Chest CT Scans Using CNNs-Based Nodule-Size-Adaptive Detection and Classification |
| title_sort | pulmonary nodule detection in volumetric chest ct scans using cnns based nodule size adaptive detection and classification |
| topic | Computed tomography pulmonary nodule object detection deep-learning convolutional neural networks |
| url | https://ieeexplore.ieee.org/document/8676287/ |
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