Medical Image Segmentation Using Deep Learning: A Survey

Oubaalla, Abdelwahid; Moubtahij, Hicham El; Akkad, Nabil El

doi:10.1007/978-3-031-29860-8_97

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…The segmentation process helps many physicians diagnose and examine many diseases. Recently, deep learning (DL) has been involved in the process of segmenting numerous medical images of the brain, breast, heart, and blood vessels [1][2][3][4][5]. It is worth noting that DL has proven particularly valuable in segmenting blood vessels in the retina, helping ophthalmologists and medical professionals in early detection of various eye and systemic diseases [6,7].…”

Section: Introductionmentioning

confidence: 99%

Multi-Layer Preprocessing and U-Net with Residual Attention Block for Retinal Blood Vessel Segmentation

Alsayat,

Elmezain,

Alanazi

et al. 2023

Diagnostics

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Retinal blood vessel segmentation is a valuable tool for clinicians to diagnose conditions such as atherosclerosis, glaucoma, and age-related macular degeneration. This paper presents a new framework for segmenting blood vessels in retinal images. The framework has two stages: a multi-layer preprocessing stage and a subsequent segmentation stage employing a U-Net with a multi-residual attention block. The multi-layer preprocessing stage has three steps. The first step is noise reduction, employing a U-shaped convolutional neural network with matrix factorization (CNN with MF) and detailed U-shaped U-Net (D_U-Net) to minimize image noise, culminating in the selection of the most suitable image based on the PSNR and SSIM values. The second step is dynamic data imputation, utilizing multiple models for the purpose of filling in missing data. The third step is data augmentation through the utilization of a latent diffusion model (LDM) to expand the training dataset size. The second stage of the framework is segmentation, where the U-Nets with a multi-residual attention block are used to segment the retinal images after they have been preprocessed and noise has been removed. The experiments show that the framework is effective at segmenting retinal blood vessels. It achieved Dice scores of 95.32, accuracy of 93.56, precision of 95.68, and recall of 95.45. It also achieved efficient results in removing noise using CNN with matrix factorization (MF) and D-U-NET according to values of PSNR and SSIM for (0.1, 0.25, 0.5, and 0.75) levels of noise. The LDM achieved an inception score of 13.6 and an FID of 46.2 in the augmentation step.

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