Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

Peter Chondro, Qazi Mazhar ul Haq, Shanq-Jang Ruan and Lieber Po-Hung Li
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Peter Chondro: Department of Electronic and Computer Eng., National Taiwan University of Science and Technology, Taipei 106, Taiwan
Qazi Mazhar ul Haq: Department of Electronic and Computer Eng., National Taiwan University of Science and Technology, Taipei 106, Taiwan
Shanq-Jang Ruan: Department of Electronic and Computer Eng., National Taiwan University of Science and Technology, Taipei 106, Taiwan
Lieber Po-Hung Li: Department of Otolaryngology, Cheng Hsin General Hospital, Taipei 112, Taiwan

Mathematics, 2020, vol. 8, issue 5, 1-15

Abstract: Maxillary sinuses are the most prevalent locations for paranasal infections on both children and adults. Common diagnostic material for this particular disease is through the screening of occipitomental-view skull radiography (SXR). With the growing cases on paranasal infections, expediting the diagnosis has become an important innovation aspect that could be addressed through the development of a computer-aided diagnosis system. As the preliminary stage of the development, an automatic segmentation over the maxillary sinuses is required to be developed. This study presents a computer-aided detection (CAD) module that segments maxillary sinuses from a plain SXR that has been preprocessed through the novel texture-based morphological analysis (ToMA). Later, the network model from the Transferable Fully Convolutional Network (T-FCN) performs pixel-wise segmentation of the maxillary sinuses. T-FCN is designed to be trained with multiple learning stages, which enables re-utilization of network weights to be adjusted based on newer dataset. According to the experiments, the proposed system achieved segmentation accuracy at 85.70%, with 50% faster learning time.

Keywords: maxillary sinus; radiography; enhancement; semantic segmentation; transfer knowledge (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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