Automated detection of nerve fiber layer defects on retinal fundus images using fully convolutional network for early diagnosis of glaucoma

Watanabe, Ryusuke; Muramatsu, Chisako; Ishida, Kyoko; Sawada, Akira; Hatanaka, Yuji; Yamamoto, Tetsuya; Fujita, Hiroshi

doi:10.1117/12.2254574

Cited by 1 publication

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When the color fundus image is used as the input of BL, compared with when the green channel is used as the input, the prediction results are improved in all the five metrics, but the improvement is not obvious. This is consistent with the results reported in [29]. When the proposed shuffle module is combined with the BL, the SU-Net has achieved significant improvements on four metrics: F-score, MAP, MIoU, and sensitivity.…”

Section: Resultssupporting

confidence: 90%

“…In the experiments, we compared the proposed CASU-Net with the following methods: FCN [29], SegNet [21], U-Net [22], M-Net [6], and CE-Net [37]. Experimental results are presented in Table. 1.…”

Section: Resultsmentioning

confidence: 99%

“…The detected RNFLD boundary pixels were fitted into lines by the random sample consensus algorithm. In [29], Watanabe et al proposed a DCNN with deconvolutional layers to detect RNFLD. DCNN training was carried out using different input image sets, such as original images of abnormal cases, original images of both normal and abnormal cases, and transformed half images.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Adaptive Weighted Loss Design in Adversarial Learning for Retinal Nerve Fiber Layer Defect Segmentation

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Glaucoma is a chronic eye disease that can cause permanent visual loss and is difficult to detect early. Retinal nerve fiber layer defect (RNFLD) is clinical evidence for the diagnosis of glaucoma. Classical deep learning based methods can be used to segment RNFLD from fundus images. However, the segmentation results of these methods do not have the specific geometry of RNFLD, and the segmentation errors of fundus images with special styles are large. In this paper, we present a novel conditional adversarial shuffle U-shaped network (CASU-Net) to segment RNFLD, which consists of a generator and a discriminator. For the generator, a mixed loss is designed, which consists of an adaptive weighted segmentation loss and an adversarial loss. This adaptive weighted segmentation loss can balance the segmentation accuracy of the target and background region, and assign more attention to the hard samples, thus ensuring the consistent improvement of the segmentation accuracy of all fundus images. The adversarial loss not only helps to improve the pixel-wise segmentation accuracy but also makes the geometry of the RNFLD segmentation closer to the ground truth. In addition, in the generator, a shuffle module was designed to fully mine the information of all channels to improve the feature extraction capability of the model. The proposed CASU-Net is verified on a RNFLD dataset from Beijing Tongren Hospital. The experiments show that the CASU-Net achieves state-of-the-art results on this dataset. INDEX TERMS Glaucoma, retinal nerve fiber layer defect segmentation, deep learning.

show abstract

Section: Resultssupporting

confidence: 90%