Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability

Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Sangil Ahn [verfasserIn] Quang T.M. Pham [verfasserIn] Jitae Shin [verfasserIn] Su Jeong Song [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	future diabetic retinopathy image synthesis prediction occurrence probability generative adversarial network

Übergeordnetes Werk:	In: Electronics - MDPI AG, 2013, 10(2021), 6, p 726
Übergeordnetes Werk:	volume:10 ; year:2021 ; number:6, p 726

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/electronics10060726

Katalog-ID:	DOAJ024054801

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allfields	10.3390/electronics10060726 doi (DE-627)DOAJ024054801 (DE-599)DOAJ155d06ead5224a8f83ae2cd985051b54 DE-627 ger DE-627 rakwb eng TK7800-8360 Sangil Ahn verfasserin aut Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model. future diabetic retinopathy image synthesis prediction occurrence probability generative adversarial network Electronics Quang T.M. Pham verfasserin aut Jitae Shin verfasserin aut Su Jeong Song verfasserin aut In Electronics MDPI AG, 2013 10(2021), 6, p 726 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:6, p 726 https://doi.org/10.3390/electronics10060726 kostenfrei https://doaj.org/article/155d06ead5224a8f83ae2cd985051b54 kostenfrei https://www.mdpi.com/2079-9292/10/6/726 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 6, p 726
spelling	10.3390/electronics10060726 doi (DE-627)DOAJ024054801 (DE-599)DOAJ155d06ead5224a8f83ae2cd985051b54 DE-627 ger DE-627 rakwb eng TK7800-8360 Sangil Ahn verfasserin aut Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model. future diabetic retinopathy image synthesis prediction occurrence probability generative adversarial network Electronics Quang T.M. Pham verfasserin aut Jitae Shin verfasserin aut Su Jeong Song verfasserin aut In Electronics MDPI AG, 2013 10(2021), 6, p 726 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:6, p 726 https://doi.org/10.3390/electronics10060726 kostenfrei https://doaj.org/article/155d06ead5224a8f83ae2cd985051b54 kostenfrei https://www.mdpi.com/2079-9292/10/6/726 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 6, p 726
allfields_unstemmed	10.3390/electronics10060726 doi (DE-627)DOAJ024054801 (DE-599)DOAJ155d06ead5224a8f83ae2cd985051b54 DE-627 ger DE-627 rakwb eng TK7800-8360 Sangil Ahn verfasserin aut Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model. future diabetic retinopathy image synthesis prediction occurrence probability generative adversarial network Electronics Quang T.M. Pham verfasserin aut Jitae Shin verfasserin aut Su Jeong Song verfasserin aut In Electronics MDPI AG, 2013 10(2021), 6, p 726 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:6, p 726 https://doi.org/10.3390/electronics10060726 kostenfrei https://doaj.org/article/155d06ead5224a8f83ae2cd985051b54 kostenfrei https://www.mdpi.com/2079-9292/10/6/726 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 6, p 726
allfieldsGer	10.3390/electronics10060726 doi (DE-627)DOAJ024054801 (DE-599)DOAJ155d06ead5224a8f83ae2cd985051b54 DE-627 ger DE-627 rakwb eng TK7800-8360 Sangil Ahn verfasserin aut Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model. future diabetic retinopathy image synthesis prediction occurrence probability generative adversarial network Electronics Quang T.M. Pham verfasserin aut Jitae Shin verfasserin aut Su Jeong Song verfasserin aut In Electronics MDPI AG, 2013 10(2021), 6, p 726 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:6, p 726 https://doi.org/10.3390/electronics10060726 kostenfrei https://doaj.org/article/155d06ead5224a8f83ae2cd985051b54 kostenfrei https://www.mdpi.com/2079-9292/10/6/726 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 6, p 726
allfieldsSound	10.3390/electronics10060726 doi (DE-627)DOAJ024054801 (DE-599)DOAJ155d06ead5224a8f83ae2cd985051b54 DE-627 ger DE-627 rakwb eng TK7800-8360 Sangil Ahn verfasserin aut Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model. future diabetic retinopathy image synthesis prediction occurrence probability generative adversarial network Electronics Quang T.M. Pham verfasserin aut Jitae Shin verfasserin aut Su Jeong Song verfasserin aut In Electronics MDPI AG, 2013 10(2021), 6, p 726 (DE-627)718626478 (DE-600)2662127-7 20799292 nnns volume:10 year:2021 number:6, p 726 https://doi.org/10.3390/electronics10060726 kostenfrei https://doaj.org/article/155d06ead5224a8f83ae2cd985051b54 kostenfrei https://www.mdpi.com/2079-9292/10/6/726 kostenfrei https://doaj.org/toc/2079-9292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2021 6, p 726
language	English
source	In Electronics 10(2021), 6, p 726 volume:10 year:2021 number:6, p 726
sourceStr	In Electronics 10(2021), 6, p 726 volume:10 year:2021 number:6, p 726
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	future diabetic retinopathy image synthesis prediction occurrence probability generative adversarial network Electronics
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authorswithroles_txt_mv	Sangil Ahn @@aut@@ Quang T.M. Pham @@aut@@ Jitae Shin @@aut@@ Su Jeong Song @@aut@@
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callnumber-first	T - Technology
author	Sangil Ahn
spellingShingle	Sangil Ahn misc TK7800-8360 misc future diabetic retinopathy image synthesis misc prediction occurrence probability misc generative adversarial network misc Electronics Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability
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topic_title	TK7800-8360 Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability future diabetic retinopathy image synthesis prediction occurrence probability generative adversarial network
topic	misc TK7800-8360 misc future diabetic retinopathy image synthesis misc prediction occurrence probability misc generative adversarial network misc Electronics
topic_unstemmed	misc TK7800-8360 misc future diabetic retinopathy image synthesis misc prediction occurrence probability misc generative adversarial network misc Electronics
topic_browse	misc TK7800-8360 misc future diabetic retinopathy image synthesis misc prediction occurrence probability misc generative adversarial network misc Electronics
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title	Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability
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title_full	Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability
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author_browse	Sangil Ahn Quang T.M. Pham Jitae Shin Su Jeong Song
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title_sort	future image synthesis for diabetic retinopathy based on the lesion occurrence probability
callnumber	TK7800-8360
title_auth	Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability
abstract	Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model.
abstractGer	Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model.
abstract_unstemmed	Diabetic Retinopathy (DR) is one of the major causes of blindness. If the lesions observed in DR occur in the central part of the fundus, it can cause severe vision loss, and we call this symptom Diabetic Macular Edema (DME). All patients with DR potentially have DME since DME can occur in every stage of DR. While synthesizing future fundus images, the task of predicting the progression of the disease state is very challenging since we need a lot of longitudinal data over a long period of time. Even if the longitudinal data are collected, there is a pixel-level difference between the current fundus image and the target future image. It is difficult to train a model based on deep learning for synthesizing future fundus images that considers the lesion change. In this paper, we synthesize future fundus images by considering the progression of the disease with a two-step training approach to overcome these problems. In the first step, we concentrate on synthesizing a realistic fundus image using only a lesion segmentation mask and vessel segmentation mask from a large dataset for a fundus generator. In the second step, we train a lesion probability predictor to create a probability map that contains the occurrence probability information of the lesion. Finally, based on the probability map and current vessel, the pre-trained fundus generator synthesizes a predicted future fundus image. We visually demonstrate not only the capacity of the fundus generator that can control the pathological information but also the prediction of the disease progression on fundus images generated by our framework. Our framework achieves an F1-score of 0.74 for predicting DR severity and 0.91 for predicting DME occurrence. We demonstrate that our framework has a meaningful capability by comparing the scores of each class of DR severity, which are obtained by passing the predicted future image and real future image through an evaluation model.
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title_short	Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability
url	https://doi.org/10.3390/electronics10060726 https://doaj.org/article/155d06ead5224a8f83ae2cd985051b54 https://www.mdpi.com/2079-9292/10/6/726 https://doaj.org/toc/2079-9292
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author2	Quang T.M. Pham Jitae Shin Su Jeong Song
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Future Image Synthesis for Diabetic Retinopathy Based on the Lesion Occurrence Probability

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