12 research outputs found
A Trio-Method for Retinal Vessel Segmentation using Image Processing
Inner Retinal neurons are a most essential part of the retina and they are
supplied with blood via retinal vessels. This paper primarily focuses on the
segmentation of retinal vessels using a triple preprocessing approach. DRIVE
database was taken into consideration and preprocessed by Gabor Filtering,
Gaussian Blur, and Edge Detection by Sobel and Pruning. Segmentation was driven
out by 2 proposed U-Net architectures. Both the architectures were compared in
terms of all the standard performance metrics. Preprocessing generated varied
interesting results which impacted the results shown by the UNet architectures
for segmentation. This real-time deployment can help in the efficient
pre-processing of images with better segmentation and detection.Comment: Accepted at 26th UK Conference on Medical Image Understanding and
Analysis (MIUA-2022) (Abstract short paper
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
The segmentation of the retinal vasculature from eye fundus images represents
one of the most fundamental tasks in retinal image analysis. Over recent years,
increasingly complex approaches based on sophisticated Convolutional Neural
Network architectures have been slowly pushing performance on well-established
benchmark datasets. In this paper, we take a step back and analyze the real
need of such complexity. Specifically, we demonstrate that a minimalistic
version of a standard U-Net with several orders of magnitude less parameters,
carefully trained and rigorously evaluated, closely approximates the
performance of current best techniques. In addition, we propose a simple
extension, dubbed W-Net, which reaches outstanding performance on several
popular datasets, still using orders of magnitude less learnable weights than
any previously published approach. Furthermore, we provide the most
comprehensive cross-dataset performance analysis to date, involving up to 10
different databases. Our analysis demonstrates that the retinal vessel
segmentation problem is far from solved when considering test images that
differ substantially from the training data, and that this task represents an
ideal scenario for the exploration of domain adaptation techniques. In this
context, we experiment with a simple self-labeling strategy that allows us to
moderately enhance cross-dataset performance, indicating that there is still
much room for improvement in this area. Finally, we also test our approach on
the Artery/Vein segmentation problem, where we again achieve results
well-aligned with the state-of-the-art, at a fraction of the model complexity
in recent literature. All the code to reproduce the results in this paper is
released
Development and validation of a deep learning algorithm using recurrent neural network for blood vessel segmentation of 3D medical image
ํ์๋
ผ๋ฌธ (์์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํ๋๊ณผ์ ๋ฐ์ด์ค์์ง๋์ด๋ง์ ๊ณต, 2021. 2. Sungwan Kim์ด๊ฒฝํธ.์ ์ฐํ๋จ์ธต์ดฌ์, ์๊ธฐ๊ณต๋ช
์์ ๋ฑ์ ์ผ์ฐจ์ ์๋ฃ์์์์ ํ๊ด ์์ญ์ถ์ถ์ ๋ค์ํ ์ฌํ๊ด๊ณ ์งํ์ ์ง๋จ ๋ฐ ์์ ๊ณํ ์๋ฆฝ์ ์๊ตฌ๋๋ ์ ํ ์ ์ฐจ์ด๋ค. ํนํ ํ๊ด์ ํ ์ฅ๊ธฐ์ ๋นํด ์ข๊ณ ๋ณต์กํ๋ค๋ ํน์ง์ด ์์ด ์์ญ์ถ์ถ์ ๋๋๊ฐ ๋๊ณ ์๋ชจ๋๋ ์ธ๋ ฅ ๋ฐ ์๊ฐ ๋ํ ์๋นํ๋ค. ๊ทธ๋ ๊ธฐ ๋๋ฌธ์ ํ๊ด ์์ญ์ถ์ถ์ ์ํ ์๋ํ ๊ธฐ์ ์ฐ๊ตฌ๋ค์ด ํ๋ฐํ ์ด๋ฃจ์ด์ง๊ณ ์๋ค.
์ต๊ทผ์๋ ๊ทธ๋ํฝ ์ฒ๋ฆฌ ์ฅ์น์ ์ํํธ์จ์ด ๊ธฐ์ ์ด ๋ฐ์ ํจ์ ๋ฐ๋ผ ์ธ๊ณต์ง๋ฅ์ผ๋ก ์๋ฃ์์์ ๋ถ์ํด ํ๋
๋ฐ ์ง๋จ์ ๋๋ ์ฐ๊ตฌ๋ถ์ผ๊ฐ ํฌ๊ฒ ํ์ฑํ ๋๊ณ ์๋ค. ๊ทธ ์ค, ํฉ์ฑ๊ณฑ์ ๊ฒฝ๋ง์ ์ฌ์ฉํ ์ฌ์ธตํ์ต ์ ๊ทผ๋ฒ์ ์์์ ๊ณต๊ฐ์ ํน์ง์ ์๋ ์ถ์ถํจ์ผ๋ก์จ ๋ฐ์ด๋ ์ฑ๋ฅ์ ๊ธฐ๋กํ๊ณ , ๋์ฝ์ฐ๊ฒฐ๋ถ๋ฅผ ์ง๋ ํน์ํ ํํ์ ํฉ์ฑ๊ณฑ์ ๊ฒฝ๋ง์ธ U-Net์ ์ธ๊ณต์ง๋ฅ ์์ญ์ถ์ถ ๋ชจ๋ธ์ ํ๋ณธ์ผ๋ก ์ธ์๋๊ณ ์๋ค.
์ผ์ฐจ์ ํ๊ด ์์ญ์ถ์ถ์ ์ํ ์ฌ์ธตํ์ต ๋ชจ๋ธ ๋ํ U-Net ๊ธฐ๋ฐ์ผ๋ก ๊ตฌ์ฑ๋๋ ๊ฒฝ์ฐ๊ฐ ์ผ๋ฐ์ ์ด๋ค. ํนํ ํ ์ฅ๊ธฐ ์์ญ์ถ์ถ์๋ ๋ฒ์ฉ์ ์ผ๋ก ์ฌ์ฉ๋๋ ์ด์ฐจ์ U-Net์ด๋ ์ผ์ฐจ์ U-Net์ ์ฑํํ๋ ์ฌ๋ก๊ฐ ๋ง๋ค. ํ์ง๋ง ํ๊ด์ ์ผ๋ฐ์ ์ธ ์ฅ๊ธฐ์ ๋ค๋ฅด๊ฒ ์ ์ฒด ๋ด์์ ๋งค์ฐ ์๊ณ ๋ณต์กํ๊ฒ ์ฝํ ์๋ค๋ ๊ตฌ์กฐ์ ํน์ง์ด ์์ด ๋ฒ์ฉ ๋ชจ๋ธ์ ๊ธฐ๋ฐ์ผ๋ก ์ ๊ทผํ ๊ฒฝ์ฐ ํ ์ฅ๊ธฐ ์์ญ์ถ์ถ์ ๋นํด ์๋์ ์ธ ์ด๋ ค์์ด ๋ฐ์ํ๋ค.
๋ณธ ์ฐ๊ตฌ์์๋ ์ผ์ฐจ์ ํ๊ด ์์ญ์ถ์ถ์ ์ฑ๋ฅ์ ํฅ์์ํค๊ธฐ ์ํด U-Net ๊ธฐ๋ฐ์ ์๋ก์ด ๋ง์ถคํ ๋ชจ๋ธ์ธ ๊ฑฐ๋ฏธํ U-Net์ ์ ์ํ๋ค. ์ด ๋ชจ๋ธ์ ํต์ฌ ์๊ณ ๋ฆฌ์ฆ์ ๊ธฐ์กด ์ด์ฐจ์ U-Net ์ ๊ทผ๋ฒ์ ์ํ์ ๊ฒฝ๋ง์ ์ฝ์
ํ๋ ๊ฒ์ด๋ค. ์ํ์ ๊ฒฝ๋ง์ ๋ณธ๋ ์์ฑ, ์์ฐ์ด ๋ฑ ์์๊ฐ ์๋ ์๋ฃ์ ๋ฌธ๋งฅ์ ์ฐ๊ฒฐ์ฑ์ ํ์ตํ๋ ์ผ์ ํนํ๋ ์ฌ์ธต์ ๊ฒฝ๋ง์ ํน์ํ ํํ์ด๋ค. ๋ฐ๋ผ์ ์ผ์ฐจ์ ์๋ฃ์์์ ์ด๋ฏธ์ง ์ํ์ค (image sequence)๋ก ๊ฐ์ฃผํ๋ค๋ฉด ์ํ์ ๊ฒฝ๋ง์ ์ ์ฉํด ์ธ์ ํ ์ด๋ฏธ์ง์์ ๋ฌธ๋งฅ์ ์ฐ๊ฒฐ์ฑ์ ๋ชจ๋ธ์ ํจ๊ณผ์ ์ผ๋ก ๋ฐ์์ํฌ ์ ์๋ค. ํนํ ํ๊ด์ ๊ธฐ๋ณธ์ ์ผ๋ก ์๋ก ์ฐ๊ฒฐ๋์ด ์์ผ๋ฏ๋ก ์ด๋ฏธ์ง ๊ฐ ๋๋ ทํ ๋ฌธ๋งฅ์ด ์กด์ฌํ๊ธฐ ๋๋ฌธ์ ์ํ์ ๊ฒฝ๋ง์ ์ฌ์ฉํ ๊ฒฝ์ฐ ํ์ต์ ํจ๊ณผ๊ฐ ์ข์์ง ์ ์๋ค.
๋ณธ ์ฐ๊ตฌ๋ ์ ์ํ ์ ๊ทผ๋ฒ์ ํจ๊ณผ ๊ฒ์ฆ์ ์ํด ๋ํ๊ด, ๊ฐํ๊ด ๋ฐ ์ข์ฌ์ค ์์ญ์ถ์ถ์ ์ํ ์ธ ์ข
๋ฅ์ ์ผ์ฐจ์ ํ๊ด ๋ฐ์ดํฐ๋ฒ ์ด์ค๋ฅผ ํ๋ณดํด ํ์ต ๋ฐ ํ๊ฐ๋ฅผ ์งํํ๋ค. ๋ํ ์ฑ๋ฅ์ ๋น๊ต๋ฅผ ์ํด ๊ธฐ์กด์ ๋ํ ๋ชจ๋ธ์ธ ์ด์ฐจ์ U-Net๊ณผ ์ผ์ฐจ์ U-Net์ ๋น๋กฏํ์ฌ ์ํ์ ๊ฒฝ๋ง์ ๋ค๋ฅธ ๋ฐฉ์์ผ๋ก ํ์ฉํ FCN-RNN์ ๋ํด ๊ฐ์ ์กฐ๊ฑด ํ์์ ์ถ๊ฐ ํ์ต์์ผ ๋น๊ต๊ตฐ์ผ๋ก ์ค์ ํ๋ค. ๋ง์ง๋ง์ผ๋ก, ์์ญ์ถ์ถ์ ์ฑ๋ฅ ํ๊ฐ์ ์ผ๋ฐ์ ์ผ๋ก ์ฌ์ฉ๋๋ ๋ค์ด์ค ๊ณ์ (DSC) ๋ฐ ์์นด๋ ๊ณ์ (IoU)์ ํ๊ท ์น๋ฅผ ๋ณด๊ณ ํ๋ค. ๋ํ์ ์ผ๋ก ๋ํ๊ด ๋ฐ์ดํฐ๋ฒ ์ด์ค์ ๋ํด ์ด์ฐจ์ U-Net์ 0.745, ์ผ์ฐจ์ U-Net์ 0.716, FCN-RNN์ 0.752, ๊ทธ๋ฆฌ๊ณ ๊ฑฐ๋ฏธํ U-Net์ 0.807์ ํ๊ท DSC๋ฅผ ๊ธฐ๋กํ๋ฉฐ ๊ธฐ์กด ๋ชจ๋ ๋ชจ๋ธ์ ์ฑ๋ฅ์ ๋ฅ๊ฐํจ์ ํ์ธํ ์ ์๋ค.3D blood vessel segmentation (3D BVS) is an important prerequisite for various cardiovascular disease diagnosis. 3D BVS is difficult because the blood vessel has complicated structure and is narrower than other organs. We propose a novel deep learning framework to improve performance by emphasizing inter-slice context that the blood vessels are connected through adjacent slices. We implemented a framework from U-Net with two structural modifications. First, the baseline is duplicated several times in parallel to extract features of adjacent slices simultaneously. Second, to weave unconnected contexts between extracted features, a LSTM layer is incorporated between encoder and decoder. Consequently, the spatial information from x-y plain and the inter-slice context from z-axis make the segmentation masks smooth and accurate. Experiment with three 3D BVS datasets shows Spider U-Net outperforms whole representative models in average dice score.๊ตญ๋ฌธ์ด๋ก i
๋ชฉ์ฐจ iv
๊ทธ๋ฆผ ๋ชฉ์ฐจ vi
ํ ๋ชฉ์ฐจ viii
1. ์๋ก 9
1.1. ๋ฐฐ๊ฒฝ 9
1.2. ์ฐ๊ตฌ์ ๋ชฉ์ 12
2. ๋ณธ๋ก 14
2.1. ์๊ณ ๋ฆฌ์ฆ 14
2.1.1. U-Net ๊ธฐ์ ๋ชจ๋ธ 15
2.1.2. ๋ ์ค๋ถ 18
2.1.3. ์จ์ค๋ถ 19
2.2. ๋ฐ์ดํฐ๋ฒ ์ด์ค 21
2.2.1. ๋ ์๊ธฐ๊ณต๋ช
ํ๊ด์กฐ์์์ 21
2.2.2. ๋ณต๋ถ ์ ์ฐํ๋จ์ธต์ดฌ์์์ 25
2.2.3. ์ฌ์ฅ ์๊ธฐ๊ณต๋ช
์์ 26
2.3. ๋น๊ต ๋ชจ๋ธ 27
2.4. ๊ตฌํ ๋ฐ ์คํ 28
3. ๊ฒฐ๊ณผ ๋ฐ ๋ถ์ 29
3.1. ์ฑ๋ฅํ๊ฐ์งํ 29
3.2. ์ ๋์ ์ฑ๋ฅํ๊ฐ 29
3.3. ์ ์ฑ์ ์ฑ๋ฅํ๊ฐ 30
3.3.1. ๋ ์๊ธฐ๊ณต๋ช
ํ๊ด์กฐ์์์ 31
3.3.2. ๋ณต๋ถ ์ ์ฐํ๋จ์ธต์ดฌ์์์ 33
3.3.3. ์ฌ์ฅ ์๊ธฐ๊ณต๋ช
์์ 34
4. ๊ณ ์ฐฐ 36
4.1. ์คํ ๊ฒฐ๊ณผ ๊ณ ์ฐฐ 36
4.2. ์๊ณ ๋ฆฌ์ฆ ๊ณ ์ฐฐ 37
5. ๊ฒฐ๋ก 38
์ฐธ๊ณ ๋ฌธํ 39
Abstract 41Maste
Deep learning based retinal vessel segmentation and hypertensive retinopathy quantification using heterogeneous features cross-attention neural network
Retinal vessels play a pivotal role as biomarkers in the detection of retinal diseases, including hypertensive retinopathy. The manual identification of these retinal vessels is both resource-intensive and time-consuming. The fidelity of vessel segmentation in automated methods directly depends on the fundus images' quality. In instances of sub-optimal image quality, applying deep learning-based methodologies emerges as a more effective approach for precise segmentation. We propose a heterogeneous neural network combining the benefit of local semantic information extraction of convolutional neural network and long-range spatial features mining of transformer network structures. Such cross-attention network structure boosts the model's ability to tackle vessel structures in the retinal images. Experiments on four publicly available datasets demonstrate our model's superior performance on vessel segmentation and the big potential of hypertensive retinopathy quantification