3D-IncNet: Head and Neck (H&N) Primary Tumors Segmentation and Survival Prediction

残余物 计算机科学 卷积(计算机科学) 分割 掷骰子 人工智能 编码器 头颈部癌 水准点(测量) 模式识别(心理学) 医学 放射科 算法 放射治疗 数学 外科 统计 大地测量学 人工神经网络 地理 操作系统
作者
Abdul Qayyum,Abdesslam Benzinou,Imran Razzak,Moona Mazher,Thanh Thi Nguyen,Domènec Puig,Fatemeh Vafaee
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:28 (3): 1185-1194 被引量:4
标识
DOI:10.1109/jbhi.2022.3219445
摘要

Cancer begins when healthy cells change and grow out of control, forming a mass called a tumor. Head and neck (H&N) cancers usually develop in or around the head and neck, including the mouth (oral cavity), nose and sinuses, throat (pharynx), and voice box (larynx). 4% of all cancers are H&N cancers with a very low survival rate (a five-year survival rate of 64.7%). FDG-PET/CT imaging is often used for early diagnosis and staging of H&N tumors, thus improving these patients' survival rates. This work presents a novel 3D-Inception-Residual aided with 3D depth-wise convolution and squeeze and excitation block. We introduce a 3D depth-wise convolution-inception encoder consisting of an additional 3D squeeze and excitation block and a 3D depth-wise convolution-based residual learning decoder (3D-IncNet), which not only helps to recalibrate the channel-wise features but adaptively through explicit inter-dependencies modeling but also integrate the coarse and fine features resulting in accurate tumor segmentation. We further demonstrate the effectiveness of inception-residual encoder-decoder architecture in achieving better dice scores and the impact of depth-wise convolution in lowering the computational cost. We applied random forest for survival prediction on deep, clinical, and radiomics features. Experiments are conducted on the benchmark HECKTOR21 challenge, which showed significantly better performance by surpassing the state-of-the-artwork and achieved 0.836 and 0.811 concordance index and dice scores, respectively. We made the model and code publicly available.
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