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12.03.2020 | Original Article Open Access

Intrapapillary capillary loop classification in magnification endoscopy: open dataset and baseline methodology

Zeitschrift:
International Journal of Computer Assisted Radiology and Surgery
Autoren:
Luis C. García-Peraza-Herrera, Martin Everson, Laurence Lovat, Hsiu-Po Wang, Wen Lun Wang, Rehan Haidry, Danail Stoyanov, Sébastien Ourselin, Tom Vercauteren
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s11548-020-02127-w) contains supplementary material, which is available to authorized users.
This work was supported through an Innovative Engineering for Health award by Wellcome Trust (WT101957); Engineering and Physical Sciences Research Council (EPSRC) (NS/A00027/1) and a Wellcome/EPSRC Centre award (203145Z/16/Z and NS/A000050/1).

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Abstract

Purpose

Early squamous cell neoplasia (ESCN) in the oesophagus is a highly treatable condition. Lesions confined to the mucosal layer can be curatively treated endoscopically. We build a computer-assisted detection system that can classify still images or video frames as normal or abnormal with high diagnostic accuracy.

Methods

We present a new benchmark dataset containing 68K binary labelled frames extracted from 114 patient videos whose imaged areas have been resected and correlated to histopathology. Our novel convolutional network architecture solves the binary classification task and explains what features of the input domain drive the decision-making process of the network.

Results

The proposed method achieved an average accuracy of 91.7% compared to the 94.7% achieved by a group of 12 senior clinicians. Our novel network architecture produces deeply supervised activation heatmaps that suggest the network is looking at intrapapillary capillary loop patterns when predicting abnormality.

Conclusion

We believe that this dataset and baseline method may serve as a reference for future benchmarks on both video frame classification and explainability in the context of ESCN detection. A future work path of high clinical relevance is the extension of the classification to ESCN types.

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