Abstract
In this paper, we have carefully investigated the clinical phenotype and genotype of patients with Johanson-Blizzard syndrome (JBS) with diabetes mellitus as the main manifestation. Retinal vessel segmentation is an important tool for the detection of many eye diseases and plays an important role in the automated screening system for retinal diseases. A segmentation algorithm based on a multiscale attentional resolution network is proposed to address the problem of insufficient segmentation of small vessels and pathological missegmentation in existing methods. The network is based on the encoder-decoder architecture, and the attention residual block is introduced in the submodule to enhance the feature propagation ability and reduce the impact of uneven illumination and low contrast on the model. The jump connection is added between the encoder and decoder, and the traditional pooling layer is removed to retain sufficient vascular detail information. Two multiscale feature fusion methods, parallel multibranch structure, and spatial pyramid pooling are used to achieve feature extraction under different sensory fields. We collected the clinical data, laboratory tests, and imaging examinations of JBS patients, extracted the genomic DNA of relevant family members, and validated them by whole-exome sequencing and Sanger sequencing. The patient had diabetes mellitus as the main manifestation, with widened eye spacing, low flat nasal root, hypoplastic nasal wing, and low hairline deformities. Genetic testing confirmed the presence of a c.4463 T > C (p.Ile1488Thr) pure missense mutation in the UBR1 gene, which was a novel mutation locus, and pathogenicity analysis indicated that the locus was pathogenic. This patient carries a new UBR1 gene c.4463 T > C pure mutation, which improves the clinical understanding of the clinical phenotypic spectrum of JBS and broadens the genetic spectrum of the UBR1 gene. The experimental results showed that the method achieved 83.26% and 82.56% F1 values on CHASEDB1 and STARE standard sets, respectively, and 83.51% and 81.20% sensitivity, respectively, and its performance was better than the current mainstream methods.
Highlights
Retinal vessel segmentation in color fundus images has been widely used for the quantitative analysis of ophthalmic diseases, such as diabetic retinopathy, the retinopathy of prematurity, hypertension, and glaucoma [1].erefore, retinal vascular segmentation plays an important role in the diagnosis of ocular-related diseases [2]
To address the problems of insufficient segmentation of small vessels and pathological missegmentation in the above literature, we propose a retinal vessel segmentation model for end-to-end training, which improves the recognition of vessel boundary information based on an encodingdecoding architecture
A segmentation algorithm based on a multiscale attentional resolution network is proposed to address the problem of insufficient segmentation of small vessels and pathological missegmentation in the existing methods. e network is based on the encoder-decoder architecture, and the attention residual block is introduced in the submodule to enhance the feature propagation ability and reduce the impact of uneven illumination and low contrast on the model. e jump connection is added between the encoder and decoder, and the traditional pooling layer is removed to retain sufficient vascular detail information
Summary
Retinal vessel segmentation in color fundus images has been widely used for the quantitative analysis of ophthalmic diseases, such as diabetic retinopathy, the retinopathy of prematurity, hypertension, and glaucoma [1]. In recent years, supervised methods based on deep learning have been applied to fundus image segmentation, showing better performance because of their ability to capture advanced semantic features, stronger data processing capability, and robustness [8–12]. E literature [10, 11] used dilated convolution to increase the sensory field of the network without using global averaging pooling in the feature extraction process It limits the ability of the network to capture global contextual information [19, 20], and it is unfavorable for accurate vascular prediction. To address the problems of insufficient segmentation of small vessels and pathological missegmentation in the above literature, we propose a retinal vessel segmentation model for end-to-end training, which improves the recognition of vessel boundary information based on an encodingdecoding architecture.
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