Semi-supervised 3D-InceptionNet for segmentation and survival prediction of head and neck primary cancers

Abstract

Cancers, known collectively as head and neck cancers, usually begin in the squamous cells that line the head and neck’s mucosal surfaces, forming a tumour mass. It usually develops in the salivary glands, nose and sinuses, voice box (larynx), throat (pharynx), or muscles or nerves in the head and neck, but these types of cancer are much less common than squamous cell carcinomas. Nearly 4% of all cancers are H&N cancers with a very low survival rate (a five-year survival rate of 64.7%). The most commonly used molecular imaging procedure for diagnosing or guiding the treatment of head and neck cancer is Fluorodeoxyglucose-positron emission tomography scanning (FDG-PET/CT), which is often used in conjunction with computed tomography (CT) scanning, and sentinel node biopsy. This work presents a semi-supervised 3D Inception-Residual framework with 3D depth-wise convolution and squeeze and excitation block. In the first phase, we performed pre-training of 3D-auto-encoder using both train and test unlabelled dataset. We, then used pre-trained weight to fine-tune the later network which is aided with depth-wise convolution-inception encoder consisting of an additional 3D squeeze and excitation block and a 3D depth-wise convolution-based residual learning decoder under deep supervision (Semi 3D-IncNet). The proposed network not only helps to recalibrate the channel-wise features adaptively through explicit inter-dependencies modelling but also integrates the coarse and fine features resulting in accurate tumour segmentation. We further demonstrate the effectiveness of semi-supervised inception-residual encoder–decoder architecture in achieving better dice scores and the impact of depth-wise convolution in lowering the computational cost. For survival prediction, we applied random forest on deep, clinical, and radiomics features. Experiments were conducted on the benchmark HECKTOR2021 and HECKTOR2022 challenge showed significantly better performance by surpassing the state-of-the-artwork and achieved 0.824/0.836 and 0.754/0.678 Dice/Concordance index for HECKTOR2021 and HECKTOR2022 respectively. We made the model and code publicly available. 11https://github.com/RespectKnowledge/HeadandNeck21_3D_Segmentation.