Polarformer: Optic Disc and Cup Segmentation Using a Hybrid CNN-Transformer and Polar Transformation

Feng, Yaowei; Li, Zhendong; Yang, Dong; Hu, Hongkai; Guo, Hui; Liu, Hao

doi:10.3390/app13010541

Open AccessArticle

Polarformer: Optic Disc and Cup Segmentation Using a Hybrid CNN-Transformer and Polar Transformation

by

Yaowei Feng

¹,

Zhendong Li

^1,2,*,

Dong Yang

¹,

Hongkai Hu

¹,

Hui Guo

^1,2 and

Hao Liu

^1,2

¹

School of Information Engineering, Ningxia University, Yinchuan 750021, China

²

Collaborative Innovation Center for Ningxia Big Data and Artificial Intelligence Co-Founded by Ningxia Municipality and Ministry of Education, Yinchuan 750021, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2023, 13(1), 541; https://doi.org/10.3390/app13010541

Submission received: 12 November 2022 / Revised: 18 December 2022 / Accepted: 25 December 2022 / Published: 30 December 2022

(This article belongs to the Special Issue Multi-Modal Deep Learning and Its Applications)

Download

Browse Figures

Versions Notes

Abstract

The segmentation of optic disc (OD) and optic cup (OC) are used in the automatic diagnosis of glaucoma. However, the spatially ambiguous boundary and semantically uncertain region-of-interest area in pictures may lead to the degradation of the performance of precise segmentation of the OC and OD. Unlike most existing methods, including the variants of CNNs (Convolutional Neural Networks) and U-Net, which limit the contributions of rich global features, we instead propose a hybrid CNN-transformer and polar transformation network, dubbed as Polarformer, which aims to extract discriminative and semantic features for robust OD and OC segmentation. Our Polarformer typically exploits contextualized features among all input units and models the correlation of structural relationships under the paradigm of the transformer backbone. More specifically, our learnable polar transformer module optimizes the polar transformations by sampling images in the Cartesian space and then mapping them back to the polar coordinate system for masked-image reconstruction. Extensive experimental results present that our Polarformer achieves superior performance in comparison to most state-of-the-art methods on three publicly available datasets.

Keywords: deep learning; multi-model learning; medical segmentation; transformer; attention

Share and Cite

MDPI and ACS Style

Feng, Y.; Li, Z.; Yang, D.; Hu, H.; Guo, H.; Liu, H. Polarformer: Optic Disc and Cup Segmentation Using a Hybrid CNN-Transformer and Polar Transformation. Appl. Sci. 2023, 13, 541. https://doi.org/10.3390/app13010541

AMA Style

Feng Y, Li Z, Yang D, Hu H, Guo H, Liu H. Polarformer: Optic Disc and Cup Segmentation Using a Hybrid CNN-Transformer and Polar Transformation. Applied Sciences. 2023; 13(1):541. https://doi.org/10.3390/app13010541

Chicago/Turabian Style

Feng, Yaowei, Zhendong Li, Dong Yang, Hongkai Hu, Hui Guo, and Hao Liu. 2023. "Polarformer: Optic Disc and Cup Segmentation Using a Hybrid CNN-Transformer and Polar Transformation" Applied Sciences 13, no. 1: 541. https://doi.org/10.3390/app13010541

APA Style

Feng, Y., Li, Z., Yang, D., Hu, H., Guo, H., & Liu, H. (2023). Polarformer: Optic Disc and Cup Segmentation Using a Hybrid CNN-Transformer and Polar Transformation. Applied Sciences, 13(1), 541. https://doi.org/10.3390/app13010541

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Menu

Polarformer: Optic Disc and Cup Segmentation Using a Hybrid CNN-Transformer and Polar Transformation

Abstract

Share and Cite

Article Metrics

Article Access Statistics

Further Information

Guidelines

MDPI Initiatives

Follow MDPI