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M2U-net: Effective and efficient retinal vessel segmentation for real-world applications
In this paper, we present a novel neural network architecture for retinal vessel segmentation that improves over the state of the art on two benchmark datasets, is the first to run in real time on high resolution images, and its small memory and processing requirements make it deployable in mobile and embedded systems. The M2U-Net has a new encoder-decoder architecture that is inspired by the U-Net. It adds pretrained components of MobileNetV2 in the encoder part and novel contractive bottleneck blocks in the decoder part that, combined with bilinear upsampling, drastically reduce the parameter count to 0.55M compared to 31.03M in the original U-Net. We have evaluated its performance against a wide body of previously published results on three public datasets. On two of them, the M2U-Net achieves new state-of-the-art performance by a considerable margin. When implemented on a GPU, our method is the first to achieve real-time inference speeds on high-resolution fundus images. We also implemented our proposed network on an ARM-based embedded system where it segments images in between 0.6 and 15 sec, depending on the resolution. Thus, the M2U-Net enables a number of applications of retinal vessel structure extraction, such as early diagnosis of eye diseases, retinal biometric authentication systems, and robot assisted microsurgery
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
LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation
Blinding eye diseases are often correlated with altered retinal morphology,
which can be clinically identified by segmenting retinal structures in fundus
images. However, current methodologies often fall short in accurately
segmenting delicate vessels. Although deep learning has shown promise in
medical image segmentation, its reliance on repeated convolution and pooling
operations can hinder the representation of edge information, ultimately
limiting overall segmentation accuracy. In this paper, we propose a lightweight
pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an
exceptionally low number of learnable parameters \textbf{(only 0.172 M)}. The
network used multipath feature extraction blocks and incorporates bidirectional
skip connections for the information flow between the encoder and decoder.
Additionally, we have optimized the efficiency of the model by carefully
selecting the number of filters to avoid filter overlap. This optimization
significantly reduces training time and enhances computational efficiency. To
assess the robustness and generalizability of LMBiS-Net, we performed
comprehensive evaluations on various aspects of retinal images. Specifically,
the model was subjected to rigorous tests to accurately segment retinal
vessels, which play a vital role in ophthalmological diagnosis and treatment.
By focusing on the retinal blood vessels, we were able to thoroughly analyze
the performance and effectiveness of the LMBiS-Net model. The results of our
tests demonstrate that LMBiS-Net is not only robust and generalizable but also
capable of maintaining high levels of segmentation accuracy. These
characteristics highlight the potential of LMBiS-Net as an efficient tool for
high-speed and accurate segmentation of retinal images in various clinical
applications
FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures
Retinal vascular segmentation, is a widely researched subject in biomedical
image processing, aims to relieve ophthalmologists' workload when treating and
detecting retinal disorders. However, segmenting retinal vessels has its own
set of challenges, with prior techniques failing to generate adequate results
when segmenting branches and microvascular structures. The neural network
approaches used recently are characterized by the inability to keep local and
global properties together and the failure to capture tiny end vessels make it
challenging to attain the desired result. To reduce this retinal vessel
segmentation problem, we propose a full-scale micro-vessel extraction mechanism
based on an encoder-decoder neural network architecture, sigmoid smoothing, and
an adaptive threshold method. The network consists of of residual, encoder
booster, bottleneck enhancement, squeeze, and excitation building blocks. All
of these blocks together help to improve the feature extraction and prediction
of the segmentation map. The proposed solution has been evaluated using the
DRIVE, CHASE-DB1, and STARE datasets, and competitive results are obtained when
compared with previous studies. The AUC and accuracy on the DRIVE dataset are
0.9884 and 0.9702, respectively. On the CHASE-DB1 dataset, the scores are
0.9903 and 0.9755, respectively. On the STARE dataset, the scores are 0.9916
and 0.9750, respectively. The performance achieved is one step ahead of what
has been done in previous studies, and this results in a higher chance of
having this solution in real-life diagnostic centers that seek ophthalmologists
attention.Comment: 7 pages, 2 figures, under consideration at Pattern Recognition
Letter
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