IterMiUnet: A lightweight architecture for automatic blood vessel segmentation

The automatic segmentation of blood vessels in fundus images can help analyze the condition of retinal vasculature, which is crucial for identifying various systemic diseases like hypertension, diabetes, etc. Despite the success of Deep Learning-based models in this segmentation task, most of them are heavily parametrized and thus have limited use in practical applications. This paper proposes IterMiUnet, a new lightweight convolution-based segmentation model that requires significantly fewer parameters and yet delivers performance similar to existing models. The model makes use of the excellent segmentation capabilities of Iternet architecture but overcomes its heavily parametrized nature by incorporating the encoder-decoder structure of MiUnet model within it. Thus, the new model reduces parameters without any compromise with the network's depth, which is necessary to learn abstract hierarchical concepts in deep models. This lightweight segmentation model speeds up training and inference time and is potentially helpful in the medical domain where data is scarce and, therefore, heavily parametrized models tend to overfit. The proposed model was evaluated on three publicly available datasets: DRIVE, STARE, and CHASE-DB1. Further cross-training and inter-rater variability evaluations have also been performed. The proposed model has a lot of potential to be utilized as a tool for the early diagnosis of many diseases

Elongated Physiological Structure Segmentation via Spatial and Scale Uncertainty-aware Network

Robust and accurate segmentation for elongated physiological structures is challenging, especially in the ambiguous region, such as the corneal endothelium microscope image with uneven illumination or the fundus image with disease interference. In this paper, we present a spatial and scale uncertainty-aware network (SSU-Net) that fully uses both spatial and scale uncertainty to highlight ambiguous regions and integrate hierarchical structure contexts. First, we estimate epistemic and aleatoric spatial uncertainty maps using Monte Carlo dropout to approximate Bayesian networks. Based on these spatial uncertainty maps, we propose the gated soft uncertainty-aware (GSUA) module to guide the model to focus on ambiguous regions. Second, we extract the uncertainty under different scales and propose the multi-scale uncertainty-aware (MSUA) fusion module to integrate structure contexts from hierarchical predictions, strengthening the final prediction. Finally, we visualize the uncertainty map of final prediction, providing interpretability for segmentation results. Experiment results show that the SSU-Net performs best on cornea endothelial cell and retinal vessel segmentation tasks. Moreover, compared with counterpart uncertainty-based methods, SSU-Net is more accurate and robust

A Comparative Study of Different Blood Vessel Detection on Retinal Images

Detection of blood vessel plays an important stage in different medical areas, such as ophthalmology, oncology, neurosurgery, and laryngology. The significance of the vessel analysis was helped by the continuous overview in clinical studies of new medical technologies intended for improving the visualization of vessels. In this paper, several local segmentation techniques which include such as Vascular Tree Extraction, Tyler L. Coye and Line tracking, Kirsch’s Template and Fuzzy C Mean methods were studied. The main objective is to determine the best approaches in order to detect the blood vessel on the degraded retinal input image (DRIVE dataset). A few Image Quality Assessment (IQA) was obtained to prove the effectiveness of each detection methods. Overall, the result of sensitivity highest came from Kirsch Templates (96.928), while specificity from Fuzzy C means (77.573). However, in term of accuracy average, the Line Tracking method is more successful compared to the other methods

Detection of Macula and Recognition of Aged-Related Macular Degeneration in Retinal Fundus Images

In aged people, the central vision is affected by Age-Related Macular Degeneration (AMD). From the digital retinal fundus images, AMD can be recognized because of the existence of Drusen, Choroidal Neovascularization (CNV), and Geographic Atrophy (GA). It is time-consuming and costly for the ophthalmologists to monitor fundus images. A monitoring system for automated digital fundus photography can reduce these problems. In this paper, we propose a new macula detection system based on contrast enhancement, top-hat transformation, and the modified Kirsch template method. Firstly, the retinal fundus image is processed through an image enhancement method so that the intensity distribution is improved for finer visualization. The contrast-enhanced image is further improved using the top-hat transformation function to make the intensities level differentiable between the macula and different sections of images. The retinal vessel is enhanced by employing the modified Kirsch's template method. It enhances the vasculature structures and suppresses the blob-like structures. Furthermore, the OTSU thresholding is used to segment out the dark regions and separate the vessel to extract the candidate regions. The dark region and the background estimated image are subtracted from the extracted blood vessels image to obtain the exact location of the macula. The proposed method applied on 1349 images of STARE, DRIVE, MESSIDOR, and DIARETDB1 databases and achieved the average sensitivity, specificity, accuracy, positive predicted value, F1 score, and area under curve of 97.79 %, 97.65 %, 97.60 %, 97.38 %, 97.57 %, and 96.97 %, respectively. Experimental results reveal that the proposed method attains better performance, in terms of visual quality and enriched quantitative analysis, in comparison with eminent state-of-the-art methods

3D Matting: A Soft Segmentation Method Applied in Computed Tomography

Three-dimensional (3D) images, such as CT, MRI, and PET, are common in medical imaging applications and important in clinical diagnosis. Semantic ambiguity is a typical feature of many medical image labels. It can be caused by many factors, such as the imaging properties, pathological anatomy, and the weak representation of the binary masks, which brings challenges to accurate 3D segmentation. In 2D medical images, using soft masks instead of binary masks generated by image matting to characterize lesions can provide rich semantic information, describe the structural characteristics of lesions more comprehensively, and thus benefit the subsequent diagnoses and analyses. In this work, we introduce image matting into the 3D scenes to describe the lesions in 3D medical images. The study of image matting in 3D modality is limited, and there is no high-quality annotated dataset related to 3D matting, therefore slowing down the development of data-driven deep-learning-based methods. To address this issue, we constructed the first 3D medical matting dataset and convincingly verified the validity of the dataset through quality control and downstream experiments in lung nodules classification. We then adapt the four selected state-of-the-art 2D image matting algorithms to 3D scenes and further customize the methods for CT images. Also, we propose the first end-to-end deep 3D matting network and implement a solid 3D medical image matting benchmark, which will be released to encourage further research.Comment: 12 pages, 7 figure