An Automated Workflow for Lung Nodule Follow-Up Recommendation Using Deep Learning

计算机科学 人工智能 雅卡索引 肺癌筛查 肺癌 分割 工作流程 假阳性率 医学 深度学习 放射科 结核(地质) 计算机断层摄影术 模式识别(心理学) 机器学习 数据库 病理 内科学 古生物学 生物
作者
Krishna Chaitanya Kaluva,Kiran Vaidhya,Abhijith Chunduru,Sambit Tarai,Sai Prasad Pranav Nadimpalli,Suthirth Vaidya
出处
期刊:Lecture Notes in Computer Science 卷期号:: 369-377 被引量:7
标识
DOI:10.1007/978-3-030-50516-5_32
摘要

Early detection of lung cancer increases a patient’s survival rate and provides healthcare professionals, valuable time, and information to administer effective treatment. Lung nodules are early signs of lung cancer. Computer-aided diagnostic systems that can identify pulmonary nodules improve early detection as well as provide an independent second opinion. We propose an automated workflow for follow-up recommendation based on low-dose computed tomography (LDCT) images using deep learning, as per 2017 Fleischner Society guidelines. As per guidelines, follow-up is based on size, volume and texture of nodules. In this paper, we present a 5 stage approach for automated follow-up recommendation. The 5 stages are Lung segmentation, Nodule detection and False Positive Reduction (FPR), Texture classification, Nodule segmentation and Follow-up recommendation. Our nodule detection has a sensitivity of 94% @ 1 false positive per scan. The FPR network improves the specificity of detection to 90% without changing sensitivity. Nodule segmentation has a Jaccard index of 0.77 on 768 nodules from Lung Nodule Database (LNDb) [1]. Texture classification has a sensitivity of 97% on solid nodules and a Fleiss-Cohen’s Kappa of 0.37 on LNDb data with most errors between sub-solid and solid nodules. Our rule-based follow-up recommendation has a Fleiss-Cohen’s Kappa of 0.53 on 236 patients from LNDb. In conclusion, we found that rule-based approach for follow-up alongside deep learning models is the best approach in achieving best results. As we improve the first 4 stages, we foresee that recommendation from AI will become closer to radiologists recommendation.
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