Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis
Retinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/10/9/1536 |
| _version_ | 1797503792601628672 |
|---|---|
| author | Muhammad Arsalan Adnan Haider Ja Hyung Koo Kang Ryoung Park |
| author_facet | Muhammad Arsalan Adnan Haider Ja Hyung Koo Kang Ryoung Park |
| author_sort | Muhammad Arsalan |
| collection | DOAJ |
| description | Retinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon disease severity, fluid or blood can leak. Similarly, hypertensive retinopathy causes a change in the retinal vasculature due to the thinning of these vessels. Central retinal vein occlusion (CRVO) is a phenomenon in which the main vein causes drainage of the blood from the retina and this main vein can close completely or partially with symptoms of blurred vision and similar eye problems. Considering the importance of the retinal vasculature as an ophthalmic disease biomarker, ophthalmologists manually analyze retinal vascular changes. Manual analysis is a tedious task that requires constant observation to detect changes. The deep learning-based methods can ease the problem by learning from the annotations provided by an expert ophthalmologist. However, current deep learning-based methods are relatively inaccurate, computationally expensive, complex, and require image preprocessing for final detection. Moreover, existing methods are unable to provide a better true positive rate (sensitivity), which shows that the model can predict most of the vessel pixels. Therefore, this study presents the so-called vessel segmentation ultra-lite network (VSUL-Net) to accurately extract the retinal vasculature from the background. The proposed VSUL-Net comprises only 0.37 million trainable parameters and uses an original image as input without preprocessing. The VSUL-Net uses a retention block that specifically maintains the larger feature map size and low-level spatial information transfer. This retention block results in better sensitivity of the proposed VSUL-Net without using expensive preprocessing schemes. The proposed method was tested on three publicly available datasets: digital retinal images for vessel extraction (DRIVE), structured analysis of retina (STARE), and children’s heart health study in England database (CHASE-DB1) for retinal vasculature segmentation. The experimental results demonstrated that VSUL-Net provides robust segmentation of retinal vasculature with sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under the curve (AUC) values of 83.80%, 98.21%, 96.95%, and 98.54%, respectively, for DRIVE, 81.73%, 98.35%, 97.17%, and 98.69%, respectively, for CHASE-DB1, and 86.64%, 98.13%, 97.27%, and 99.01%, respectively, for STARE datasets. The proposed method provides an accurate segmentation mask for deep ophthalmic analysis. |
| first_indexed | 2024-03-10T03:55:24Z |
| format | Article |
| id | doaj.art-34656bcec6f14217ba5a70006b036195 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T03:55:24Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-34656bcec6f14217ba5a70006b0361952023-11-23T08:45:48ZengMDPI AGMathematics2227-73902022-05-01109153610.3390/math10091536Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic AnalysisMuhammad Arsalan0Adnan Haider1Ja Hyung Koo2Kang Ryoung Park3Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Seoul 04620, KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Seoul 04620, KoreaRetinal blood vessels possess a complex structure in the retina and are considered an important biomarker for several retinal diseases. Ophthalmic diseases result in specific changes in the retinal vasculature; for example, diabetic retinopathy causes the retinal vessels to swell, and depending upon disease severity, fluid or blood can leak. Similarly, hypertensive retinopathy causes a change in the retinal vasculature due to the thinning of these vessels. Central retinal vein occlusion (CRVO) is a phenomenon in which the main vein causes drainage of the blood from the retina and this main vein can close completely or partially with symptoms of blurred vision and similar eye problems. Considering the importance of the retinal vasculature as an ophthalmic disease biomarker, ophthalmologists manually analyze retinal vascular changes. Manual analysis is a tedious task that requires constant observation to detect changes. The deep learning-based methods can ease the problem by learning from the annotations provided by an expert ophthalmologist. However, current deep learning-based methods are relatively inaccurate, computationally expensive, complex, and require image preprocessing for final detection. Moreover, existing methods are unable to provide a better true positive rate (sensitivity), which shows that the model can predict most of the vessel pixels. Therefore, this study presents the so-called vessel segmentation ultra-lite network (VSUL-Net) to accurately extract the retinal vasculature from the background. The proposed VSUL-Net comprises only 0.37 million trainable parameters and uses an original image as input without preprocessing. The VSUL-Net uses a retention block that specifically maintains the larger feature map size and low-level spatial information transfer. This retention block results in better sensitivity of the proposed VSUL-Net without using expensive preprocessing schemes. The proposed method was tested on three publicly available datasets: digital retinal images for vessel extraction (DRIVE), structured analysis of retina (STARE), and children’s heart health study in England database (CHASE-DB1) for retinal vasculature segmentation. The experimental results demonstrated that VSUL-Net provides robust segmentation of retinal vasculature with sensitivity (Sen), specificity (Spe), accuracy (Acc), and area under the curve (AUC) values of 83.80%, 98.21%, 96.95%, and 98.54%, respectively, for DRIVE, 81.73%, 98.35%, 97.17%, and 98.69%, respectively, for CHASE-DB1, and 86.64%, 98.13%, 97.27%, and 99.01%, respectively, for STARE datasets. The proposed method provides an accurate segmentation mask for deep ophthalmic analysis.https://www.mdpi.com/2227-7390/10/9/1536retinafundus imageretinal vasculatureretinal disorderssemantic segmentation |
| spellingShingle | Muhammad Arsalan Adnan Haider Ja Hyung Koo Kang Ryoung Park Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis Mathematics retina fundus image retinal vasculature retinal disorders semantic segmentation |
| title | Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis |
| title_full | Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis |
| title_fullStr | Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis |
| title_full_unstemmed | Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis |
| title_short | Segmenting Retinal Vessels Using a Shallow Segmentation Network to Aid Ophthalmic Analysis |
| title_sort | segmenting retinal vessels using a shallow segmentation network to aid ophthalmic analysis |
| topic | retina fundus image retinal vasculature retinal disorders semantic segmentation |
| url | https://www.mdpi.com/2227-7390/10/9/1536 |
| work_keys_str_mv | AT muhammadarsalan segmentingretinalvesselsusingashallowsegmentationnetworktoaidophthalmicanalysis AT adnanhaider segmentingretinalvesselsusingashallowsegmentationnetworktoaidophthalmicanalysis AT jahyungkoo segmentingretinalvesselsusingashallowsegmentationnetworktoaidophthalmicanalysis AT kangryoungpark segmentingretinalvesselsusingashallowsegmentationnetworktoaidophthalmicanalysis |