Automatic CT image segmentation of maxillary sinus based on VGG network and improved V-Net.

International Journal of Computer Assisted Radiology and Surgery
Jiangchang XuXiaojun Chen

Abstract

The analysis of the maxillary sinus (MS) can provide an assessment for many clinical diagnoses, so accurate CT image segmentation of the MS is essential. However, common segmentation methods are mainly done by experienced doctors manually, and there are some challenges such as low efficiency and precision. As for automatic methods, the initial seed points and adjustment of various parameters are required, which will affect the segmentation efficiency. Thus, accurate, efficient, and automatic segmentation method of MS is critical to promote the clinical application. This paper proposed an automatic CT image segmentation method of MS based on VGG network and improved V-Net. The VGG network was established to classify CT slices, which can avoid the failure of CT slice segmentation without MS. Then, we proposed the improved V-Net based on edge supervision for segmenting MS regions more effectively. The edge loss was integrated into the loss of the improved V-Net, which could reduce region misjudgment and improve the automatic segmentation performance. For the classification of CT slices with MS and without MS, the VGG network had a classification accuracy of 97.04 ± 2.03%. In the segmentation, our method obtained a better result, i...Continue Reading

References

Mar 17, 2015·Surgical and Radiologic Anatomy : SRA·Uygar Levent DemirB Hakyemez
Aug 1, 2015·Journal of Maxillofacial and Oral Surgery·Antoine BerberiZiad Salameh
Jun 1, 2016·IEEE Transactions on Pattern Analysis and Machine Intelligence·Evan ShelhamerTrevor Darrell
Nov 17, 2017·Journal of Oral & Maxillofacial Research·Thomas Starch-Jensen, Janek Dalsgaard Jensen
Jan 6, 2018·PloS One·Guilherme GiacominiDiana Rodrigues de Pina
Feb 9, 2018·The British Journal of Radiology·Tobias N AndersenSven Kreiborg
Jun 27, 2018·International Journal of Legal Medicine·Amanda Farias GomesRosana de Fátima Possobon
Sep 19, 2019·Expert Review of Medical Devices·Jiangchang XuXiaojun Chen

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Citations

Aug 8, 2021·Journal of Clinical Medicine·Hayoung ByunTae Hyun Kim

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