443 research outputs found
OneSeg: Self-learning and One-shot Learning based Single-slice Annotation for 3D Medical Image Segmentation
As deep learning methods continue to improve medical image segmentation
performance, data annotation is still a big bottleneck due to the
labor-intensive and time-consuming burden on medical experts, especially for 3D
images. To significantly reduce annotation efforts while attaining competitive
segmentation accuracy, we propose a self-learning and one-shot learning based
framework for 3D medical image segmentation by annotating only one slice of
each 3D image. Our approach takes two steps: (1) self-learning of a
reconstruction network to learn semantic correspondence among 2D slices within
3D images, and (2) representative selection of single slices for one-shot
manual annotation and propagating the annotated data with the well-trained
reconstruction network. Extensive experiments verify that our new framework
achieves comparable performance with less than 1% annotated data compared with
fully supervised methods and generalizes well on several out-of-distribution
testing sets
Attention Mechanisms in Medical Image Segmentation: A Survey
Medical image segmentation plays an important role in computer-aided
diagnosis. Attention mechanisms that distinguish important parts from
irrelevant parts have been widely used in medical image segmentation tasks.
This paper systematically reviews the basic principles of attention mechanisms
and their applications in medical image segmentation. First, we review the
basic concepts of attention mechanism and formulation. Second, we surveyed over
300 articles related to medical image segmentation, and divided them into two
groups based on their attention mechanisms, non-Transformer attention and
Transformer attention. In each group, we deeply analyze the attention
mechanisms from three aspects based on the current literature work, i.e., the
principle of the mechanism (what to use), implementation methods (how to use),
and application tasks (where to use). We also thoroughly analyzed the
advantages and limitations of their applications to different tasks. Finally,
we summarize the current state of research and shortcomings in the field, and
discuss the potential challenges in the future, including task specificity,
robustness, standard evaluation, etc. We hope that this review can showcase the
overall research context of traditional and Transformer attention methods,
provide a clear reference for subsequent research, and inspire more advanced
attention research, not only in medical image segmentation, but also in other
image analysis scenarios.Comment: Submitted to Medical Image Analysis, survey paper, 34 pages, over 300
reference
DSFNet: Convolutional Encoder-Decoder Architecture Combined Dual-GCN and Stand-alone Self-attention by Fast Normalized Fusion for Polyps Segmentation
In the past few decades, deep learning technology has been widely used in
medical image segmentation and has made significant breakthroughs in the fields
of liver and liver tumor segmentation, brain and brain tumor segmentation,
video disc segmentation, heart image segmentation, and so on. However, the
segmentation of polyps is still a challenging task since the surface of the
polyps is flat and the color is very similar to that of surrounding tissues.
Thus, It leads to the problems of the unclear boundary between polyps and
surrounding mucosa, local overexposure, and bright spot reflection. To counter
this problem, this paper presents a novel U-shaped network, namely DSFNet,
which effectively combines the advantages of Dual-GCN and self-attention
mechanisms. First, we introduce a feature enhancement block module based on
Dual-GCN module as an attention mechanism to enhance the feature extraction of
local spatial and structural information with fine granularity. Second, the
stand-alone self-attention module is designed to enhance the integration
ability of the decoding stage model to global information. Finally, the Fast
Normalized Fusion method with trainable weights is used to efficiently fuse the
corresponding three feature graphs in encoding, bottleneck, and decoding
blocks, thus promoting information transmission and reducing the semantic gap
between encoder and decoder. Our model is tested on two public datasets
including Endoscene and Kvasir-SEG and compared with other state-of-the-art
models. Experimental results show that the proposed model surpasses other
competitors in many indicators, such as Dice, MAE, and IoU. In the meantime,
ablation studies are also conducted to verify the efficacy and effectiveness of
each module. Qualitative and quantitative analysis indicates that the proposed
model has great clinical significance.Comment: 10 pages, 6 figures, 3 table
A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology
In the cancer diagnosis pipeline, digital pathology plays an instrumental
role in the identification, staging, and grading of malignant areas on biopsy
tissue specimens. High resolution histology images are subject to high variance
in appearance, sourcing either from the acquisition devices or the H\&E
staining process. Nuclei segmentation is an important task, as it detects the
nuclei cells over background tissue and gives rise to the topology, size, and
count of nuclei which are determinant factors for cancer detection. Yet, it is
a fairly time consuming task for pathologists, with reportedly high
subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern
Artificial Intelligence (AI) models enable the automation of nuclei
segmentation. This can reduce the subjectivity in analysis and reading time.
This paper provides an extensive review, beginning from earlier works use
traditional image processing techniques and reaching up to modern approaches
following the Deep Learning (DL) paradigm. Our review also focuses on the weak
supervision aspect of the problem, motivated by the fact that annotated data is
scarce. At the end, the advantages of different models and types of supervision
are thoroughly discussed. Furthermore, we try to extrapolate and envision how
future research lines will potentially be, so as to minimize the need for
labeled data while maintaining high performance. Future methods should
emphasize efficient and explainable models with a transparent underlying
process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table
Deep segmentation of the liver and the hepatic tumors from abdomen tomography images
A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation
Medical Image Segmentation Review: The success of U-Net
Automatic medical image segmentation is a crucial topic in the medical domain
and successively a critical counterpart in the computer-aided diagnosis
paradigm. U-Net is the most widespread image segmentation architecture due to
its flexibility, optimized modular design, and success in all medical image
modalities. Over the years, the U-Net model achieved tremendous attention from
academic and industrial researchers. Several extensions of this network have
been proposed to address the scale and complexity created by medical tasks.
Addressing the deficiency of the naive U-Net model is the foremost step for
vendors to utilize the proper U-Net variant model for their business. Having a
compendium of different variants in one place makes it easier for builders to
identify the relevant research. Also, for ML researchers it will help them
understand the challenges of the biological tasks that challenge the model. To
address this, we discuss the practical aspects of the U-Net model and suggest a
taxonomy to categorize each network variant. Moreover, to measure the
performance of these strategies in a clinical application, we propose fair
evaluations of some unique and famous designs on well-known datasets. We
provide a comprehensive implementation library with trained models for future
research. In addition, for ease of future studies, we created an online list of
U-Net papers with their possible official implementation. All information is
gathered in https://github.com/NITR098/Awesome-U-Net repository.Comment: Submitted to the IEEE Transactions on Pattern Analysis and Machine
Intelligence Journa
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