A Novel Three-way fusion image segmentation for early esophageal cancer detection

Objective Esophageal cancer (EC) is a prevalent malignancy worldwide. Early-stage esophageal cancer (EEC) diagnostics is crucial for improving patient survival. However, EC is highly aggressive with a poor prognosis, even for experienced endoscopists. To address these problems, this study aims to develop a novel computer-aided diagnosis (CAD) method to improve the accuracy and efficiency of EEC diagnostics. Methods Three-way fusion CAD method that employs multiple frameworks, including the hybrid task cascade ResNeXt101 with deformable convolutional networks, to accurately detect EC. Our method incorporates dual annotation categories on ME-NBI imaging from a local perspective and one category on LCE imaging from an broader perspective. This integration provides a substantial improvement of accuracy over traditional CAD technologies. Results Our three-way fusion CAD method achieved top performances of 0.923 mAP on ME-NBI and 0.862 mAP on LCE, demonstrating superior diagnostic performance compared to traditional CAD methods. Furthermore, the treatment boundary mAP is expected to be even higher by definition in clinical settings. Our method also achieved promising precision and recall rates of 93.98% and 93.05% for ME-NBI, and 82.89% and 88.32% for LCE, respectively. Conclusions Our novel three-way fusion CAD method accurately detects EC in both ME-NBI and LCE imaging, providing accurate treatment boundaries on both image and patient levels. Our approach shows potential for clinical application, with promising mAP, precision, and recall rates. Further work will focus on collecting and analyzing patient data to improve the method’s real-time performance in clinical settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study was funded by Jinan healthcare science and technology plan(201907046), Shandong Province key research and development program (2019GSF108028) and Youth Natural Science Foundation of Shandong Province (ZR2020QH040). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of The first affiliated hospital of Shandong first Medical University (Shandong Provincial Qianfoshan hospital) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.