1,895 research outputs found
U-Net and its variants for medical image segmentation: theory and applications
U-net is an image segmentation technique developed primarily for medical
image analysis that can precisely segment images using a scarce amount of
training data. These traits provide U-net with a very high utility within the
medical imaging community and have resulted in extensive adoption of U-net as
the primary tool for segmentation tasks in medical imaging. The success of
U-net is evident in its widespread use in all major image modalities from CT
scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a
segmentation tool, there have been instances of the use of U-net in other
applications. As the potential of U-net is still increasing, in this review we
look at the various developments that have been made in the U-net architecture
and provide observations on recent trends. We examine the various innovations
that have been made in deep learning and discuss how these tools facilitate
U-net. Furthermore, we look at image modalities and application areas where
U-net has been applied.Comment: 42 pages, in IEEE Acces
Channel Attention Separable Convolution Network for Skin Lesion Segmentation
Skin cancer is a frequently occurring cancer in the human population, and it
is very important to be able to diagnose malignant tumors in the body early.
Lesion segmentation is crucial for monitoring the morphological changes of skin
lesions, extracting features to localize and identify diseases to assist
doctors in early diagnosis. Manual de-segmentation of dermoscopic images is
error-prone and time-consuming, thus there is a pressing demand for precise and
automated segmentation algorithms. Inspired by advanced mechanisms such as
U-Net, DenseNet, Separable Convolution, Channel Attention, and Atrous Spatial
Pyramid Pooling (ASPP), we propose a novel network called Channel Attention
Separable Convolution Network (CASCN) for skin lesions segmentation. The
proposed CASCN is evaluated on the PH2 dataset with limited images. Without
excessive pre-/post-processing of images, CASCN achieves state-of-the-art
performance on the PH2 dataset with Dice similarity coefficient of 0.9461 and
accuracy of 0.9645.Comment: Accepted by ICONIP 202
IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation
Skin lesion segmentation plays a crucial role in the computer-aided diagnosis
of melanoma. Deep Learning models have shown promise in accurately segmenting
skin lesions, but their widespread adoption in real-life clinical settings is
hindered by their inherent black-box nature. In domains as critical as
healthcare, interpretability is not merely a feature but a fundamental
requirement for model adoption. This paper proposes IARS SegNet an advanced
segmentation framework built upon the SegNet baseline model. Our approach
incorporates three critical components: Skip connections, residual
convolutions, and a global attention mechanism onto the baseline Segnet
architecture. These elements play a pivotal role in accentuating the
significance of clinically relevant regions, particularly the contours of skin
lesions. The inclusion of skip connections enhances the model's capacity to
learn intricate contour details, while the use of residual convolutions allows
for the construction of a deeper model while preserving essential image
features. The global attention mechanism further contributes by extracting
refined feature maps from each convolutional and deconvolutional block, thereby
elevating the model's interpretability. This enhancement highlights critical
regions, fosters better understanding, and leads to more accurate skin lesion
segmentation for melanoma diagnosis.Comment: Submitted to the journal: Computers in Biology and Medicin
Attention Gated Networks: Learning to Leverage Salient Regions in Medical Images
We propose a novel attention gate (AG) model for medical image analysis that
automatically learns to focus on target structures of varying shapes and sizes.
Models trained with AGs implicitly learn to suppress irrelevant regions in an
input image while highlighting salient features useful for a specific task.
This enables us to eliminate the necessity of using explicit external
tissue/organ localisation modules when using convolutional neural networks
(CNNs). AGs can be easily integrated into standard CNN models such as VGG or
U-Net architectures with minimal computational overhead while increasing the
model sensitivity and prediction accuracy. The proposed AG models are evaluated
on a variety of tasks, including medical image classification and segmentation.
For classification, we demonstrate the use case of AGs in scan plane detection
for fetal ultrasound screening. We show that the proposed attention mechanism
can provide efficient object localisation while improving the overall
prediction performance by reducing false positives. For segmentation, the
proposed architecture is evaluated on two large 3D CT abdominal datasets with
manual annotations for multiple organs. Experimental results show that AG
models consistently improve the prediction performance of the base
architectures across different datasets and training sizes while preserving
computational efficiency. Moreover, AGs guide the model activations to be
focused around salient regions, which provides better insights into how model
predictions are made. The source code for the proposed AG models is publicly
available.Comment: Accepted for Medical Image Analysis (Special Issue on Medical Imaging
with Deep Learning). arXiv admin note: substantial text overlap with
arXiv:1804.03999, arXiv:1804.0533
- …