File(s) under permanent embargo
3D-IncNet: Head and Neck (H&N) Primary Tumors Segmentation and Survival Prediction
journal contribution
posted on 2023-02-08, 23:37 authored by A Qayyum, A Benzinou, Imran RazzakImran Razzak, M Mazher, Thanh Thi NguyenThanh Thi Nguyen, D Puig, F VafaeeCancer 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 11
https://github.com/RespectKnowledge/HeadandNeck21_3D_Segmentation
.History
Journal
IEEE Journal of Biomedical and Health InformaticsVolume
PPPagination
1-9Publisher DOI
ISSN
2168-2194eISSN
2168-2208Issue
99Publisher
Institute of Electrical and Electronics Engineers (IEEE)Usage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC