6 research outputs found
Curvilinear object segmentation in medical images based on ODoS filter and deep learning network
Automatic segmentation of curvilinear objects in medical images plays an
important role in the diagnosis and evaluation of human diseases, yet it is a
challenging uncertainty in the complex segmentation tasks due to different
issues such as various image appearances, low contrast between curvilinear
objects and their surrounding backgrounds, thin and uneven curvilinear
structures, and improper background illumination conditions. To overcome these
challenges, we present a unique curvilinear structure segmentation framework
based on an oriented derivative of stick (ODoS) filter and a deep learning
network for curvilinear object segmentation in medical images. Currently, a
large number of deep learning models emphasize developing deep architectures
and ignore capturing the structural features of curvilinear objects, which may
lead to unsatisfactory results. Consequently, a new approach that incorporates
an ODoS filter as part of a deep learning network is presented to improve the
spatial attention of curvilinear objects. Specifically, the input image is
transfered into four-channel image constructed by the ODoS filter. In which,
the original image is considered the principal part to describe various image
appearance and complex background illumination conditions, a multi-step
strategy is used to enhance the contrast between curvilinear objects and their
surrounding backgrounds, and a vector field is applied to discriminate thin and
uneven curvilinear structures. Subsequently, a deep learning framework is
employed to extract various structural features for curvilinear object
segmentation in medical images. The performance of the computational model is
validated in experiments conducted on the publicly available DRIVE, STARE and
CHASEDB1 datasets. The experimental results indicate that the presented model
yields surprising results compared with those of some state-of-the-art methods.Comment: 20 pages, 8 figure
Segmentation of fundus vascular images based on a dual-attention mechanism
Accurately segmenting blood vessels in retinal fundus images is crucial in
the early screening, diagnosing, and evaluating some ocular diseases. However,
significant light variations and non-uniform contrast in these images make
segmentation quite challenging. Thus, this paper employ an attention fusion
mechanism that combines the channel attention and spatial attention mechanisms
constructed by Transformer to extract information from retinal fundus images in
both spatial and channel dimensions. To eliminate noise from the encoder image,
a spatial attention mechanism is introduced in the skip connection. Moreover, a
Dropout layer is employed to randomly discard some neurons, which can prevent
overfitting of the neural network and improve its generalization performance.
Experiments were conducted on publicly available datasets DERIVE, STARE, and
CHASEDB1. The results demonstrate that our method produces satisfactory results
compared to some recent retinal fundus image segmentation algorithms.Comment: 17 pages,6 figure