Learning-based approach to segment pigment signs in fundus images for Retinitis Pigmentosa analysis

Frucci, Maria; Gragnaniello, Diego; Riccio, Daniel; Iorio, Valentina Di; Perna, Luigi Di; Simonelli, Francesca

doi:10.1016/j.neucom.2018.04.065

Cited by 12 publications

(20 citation statements)

References 37 publications

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“…U-Net) [47] Subsequently improved segmentation performance by modified U-Net model, and 15% improvement in F-measure compared to [46].…”

Section: Brancati Et Al (Modifiedmentioning

confidence: 97%

“…Because of the unavailability of the public dataset for RP and pigment landmarks, few researchers have focused on learning-based methods for retinal pigment segmentation with fundus images for RP analysis. Brancati et al [46] innovatively constructed the Retinal Images for Pigment Signs (RIPS) dataset for segmentation of retinal pigments and detection of RP in fundus images. They provided baseline learning-based methods for the researchers, which contributed to RP analysis.…”

Section: Retinal Image Segmentation Based On Deep-feature (Cnn)mentioning

confidence: 99%

“…The strength of this method is the simplicity of the ensemble learning and the advantage over the classification trees. As false negatives are more important in medical applications, so the Random Forest classifier shows more false negatives compared to AdaBoost [46]. The same group subsequently enhanced the accuracy further by using a modified U-Net deep learning model on patches [47].…”

Section: Retinal Image Segmentation Based On Deep-feature (Cnn)mentioning

confidence: 99%

“…Brancati et al [46] The simple machine learning classifier are used, AdaBoost provides less false negatives…”

Section: Rp By Learned Featuresmentioning

confidence: 99%

“…The method performance is affected by more false negatives (represented by sensitivity of the method) compared to [46].…”

Section: Brancati Et Al (Modifiedmentioning

confidence: 99%

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Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa

Arsalan

Baek

Owais

et al. 2020

Sensors

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Ophthalmological analysis plays a vital role in the diagnosis of various eye diseases, such as glaucoma, retinitis pigmentosa (RP), and diabetic and hypertensive retinopathy. RP is a genetic retinal disorder that leads to progressive vision degeneration and initially causes night blindness. Currently, the most commonly applied method for diagnosing retinal diseases is optical coherence tomography (OCT)-based disease analysis. In contrast, fundus imaging-based disease diagnosis is considered a low-cost diagnostic solution for retinal diseases. This study focuses on the detection of RP from the fundus image, which is a crucial task because of the low quality of fundus images and non-cooperative image acquisition conditions. Automatic detection of pigment signs in fundus images can help ophthalmologists and medical practitioners in diagnosing and analyzing RP disorders. To accurately segment pigment signs for diagnostic purposes, we present an automatic RP segmentation network (RPS-Net), which is a specifically designed deep learning-based semantic segmentation network to accurately detect and segment the pigment signs with fewer trainable parameters. Compared with the conventional deep learning methods, the proposed method applies a feature enhancement policy through multiple dense connections between the convolutional layers, which enables the network to discriminate between normal and diseased eyes, and accurately segment the diseased area from the background. Because pigment spots can be very small and consist of very few pixels, the RPS-Net provides fine segmentation, even in the case of degraded images, by importing high-frequency information from the preceding layers through concatenation inside and outside the encoder-decoder. To evaluate the proposed RPS-Net, experiments were performed based on 4-fold cross-validation using the publicly available Retinal Images for Pigment Signs (RIPS) dataset for detection and segmentation of retinal pigments. Experimental results show that RPS-Net achieved superior segmentation performance for RP diagnosis, compared with the state-of-the-art methods.

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“…U-Net) [47] Subsequently improved segmentation performance by modified U-Net model, and 15% improvement in F-measure compared to [46].…”

Section: Brancati Et Al (Modifiedmentioning

confidence: 97%

Section: Retinal Image Segmentation Based On Deep-feature (Cnn)mentioning

confidence: 99%

Section: Retinal Image Segmentation Based On Deep-feature (Cnn)mentioning

confidence: 99%

“…Brancati et al [46] The simple machine learning classifier are used, AdaBoost provides less false negatives…”

Section: Rp By Learned Featuresmentioning

confidence: 99%

“…The method performance is affected by more false negatives (represented by sensitivity of the method) compared to [46].…”

Section: Brancati Et Al (Modifiedmentioning

confidence: 99%

See 3 more Smart Citations

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa

Arsalan

Baek

Owais

et al. 2020

Sensors

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show abstract

Segmentation of Pigment Signs in Fundus Images for Retinitis Pigmentosa Analysis by Using Deep Learning

Frucci

Riccio

Perna

et al. 2019

Lecture Notes in Computer Science

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Evaluating Intelligent Methods for Decision Making Support in Dermoscopy Based on Information Gain and Ensemble

Spolaôr

Fonseca-Pinto

Mendes

et al. 2021

Computational Intelligence for Business Analytics

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Learning-based approach to segment pigment signs in fundus images for Retinitis Pigmentosa analysis

Cited by 12 publications

References 37 publications

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa

Deep Learning-Based Detection of Pigment Signs for Analysis and Diagnosis of Retinitis Pigmentosa

Segmentation of Pigment Signs in Fundus Images for Retinitis Pigmentosa Analysis by Using Deep Learning

Evaluating Intelligent Methods for Decision Making Support in Dermoscopy Based on Information Gain and Ensemble

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