Degradation-Invariant Enhancement of Fundus Images via Pyramid Constraint Network

Liu，Haofeng1,2; Li，Heng2; Fu，Huazhu3; Xiao，Ruoxiu4; Gao，Yunshu1,2; Hu，Yan2; Liu，Jiang1,2,5

10.1007/978-3-031-16434-7_49

2022

25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)

0302-9743

1611-3349

978-3-031-16433-0

LECT NOTES ARTIF INT

13432 LNCS

507-516

SEP 18-22, 2022

null,Singapore,SINGAPORE

GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND

SPRINGER INTERNATIONAL PUBLISHING AG

As an economical and efficient fundus imaging modality, retinal fundus images have been widely adopted in clinical fundus examination. Unfortunately, fundus images often suffer from quality degradation caused by imaging interferences, leading to misdiagnosis. Despite impressive enhancement performances that state-of-the-art methods have achieved, challenges remain in clinical scenarios. For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data. Firstly, high-quality images are randomly degraded to form sequences of low-quality ones sharing the same content (SeqLCs). Then individual low-quality images are decomposed to Laplacian pyramid features (LPF) as the multi-level input for the enhancement. Subsequently, a feature pyramid constraint (FPC) for the sequence is introduced to enforce the PCE-Net to learn a degradation-invariant model. Extensive experiments have been conducted under the evaluation metrics of enhancement and segmentation. The effectiveness of the PCE-Net was demonstrated in comparison with state-of-the-art methods and the ablation study. The source code of this study is publicly available at https://github.com/HeverLaw/PCENet-Image-Enhancement.

Degradation-invariant enhancement Feature pyramid constraint Fundus image Laplacian pyramid feature

First ; Corresponding

English

CPCI-S

Basic and Applied Fundamental Research Foundation of Guangdong Province[2020A1515110286] ; National Natural Science Foundation of China[8210072776] ; Guangdong Provincial Department of Education[2020ZDZX3043] ; Guangdong Provincial Key Laboratory[2020B121201001] ; Shenzhen Natural Science Fund["JCYJ20200109140820699","20200925174052004"] ; A*STAR AME Programmatic Fund[A20H4b0141]

Computer Science ; Radiology, Nuclear Medicine & Medical Imaging

Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications ; Radiology, Nuclear Medicine & Medical Imaging

WOS:000867288800049

2-s2.0-85139075694

Scopus

1.Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation,Southern University of Science and Technology,Shenzhen,China
2.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,China
3.IHPC,A*STAR,Singapore,Singapore
4.The School of Computer and Communication Engineering,University of Science and Technology Beijing,Beijing,China
5.Singapore Eye Research Institute,Singapore National Eye Centre,Singapore,Singapore

Liu，Haofeng,Li，Heng,Fu，Huazhu,et al. Degradation-Invariant Enhancement of Fundus Images via Pyramid Constraint Network[C]. GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND:SPRINGER INTERNATIONAL PUBLISHING AG,2022:507-516.