Towards an Automatic Lung Cancer Screening System in Low Dose Computed Tomography

被引：11

作者：

Aresta, Guilherme ^{[1
,2
]}

Araujo, Teresa ^{[1
,2
]}

Jacobs, Colin ^{[6
]}

van Ginneken, Bram ^{[6
]}

Cunha, Antonio ^{[1
,3
,4
]}

Ramos, Isabel ^{[5
]}

Campilho, Aurelio ^{[1
,2
]}

机构：

[1] INESC TEC, P-4200 Porto, Portugal

[2] Univ Porto, Fac Engn, P-4200465 Porto, Portugal

[3] Univ Minho, P-5001801 Vila Real, Portugal

[4] Alto Douro, P-5001801 Vila Real, Portugal

[5] Univ Porto, Fac Med, P-4200319 Porto, Portugal

[6] Radboud Univ Nijmegen, Med Ctr, NL-6525 Nijmegen, Netherlands

来源：

IMAGE ANALYSIS FOR MOVING ORGAN, BREAST, AND THORACIC IMAGES | 2018年 / 11040卷

关键词：

Computer aided diagnosis; Lung cancer; Low dose computed tomography images; Screening; Deep learning; IMAGE DATABASE CONSORTIUM; PULMONARY NODULES; MORTALITY;

D O I：

10.1007/978-3-030-00946-5_31

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We propose a deep learning-based pipeline that, given a low-dose computed tomography of a patient chest, recommends if a patient should be submitted to further lung cancer assessment. The algorithm is composed of a nodule detection block that uses the object detection framework YOLOv2, followed by a U-Net based segmentation. The found structures of interest are then characterized in terms of diameter and texture to produce a final referral recommendation according to the National Lung Screen Trial (NLST) criteria. Our method is trained using the public LUNA16 and LIDC-IDRI datasets and tested on an independent dataset composed of 500 scans from the Kaggle DSB 2017 challenge. The proposed system achieves a patient-wise recall of 89% while providing an explanation to the referral decision and thus may serve as a second opinion tool to speed-up and improve lung cancer screening.

引用

页码：310 / 318

页数：9

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