DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation

Lou, Ange; Guan, Shuyue; Loew, Murray H.

doi:10.1117/12.2582338

Cited by 91 publications

(67 citation statements)

References 24 publications

(27 reference statements)

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“…Although good results have been achieved, there is still a long way to go before its use in practical application is viable. However, given the excellent structure of the U-Net model, many excellent variants have emerged, such as ResUNet-a [ 19 ] and DC-UNet [ 13 ]. Further research directions include using a better model than MultiResUNet, adding more training data, or using a loss function that is more suitable for intervertebral disc segmentation in order to improve the accuracy of the segmentation.…”

Section: Discussionmentioning

confidence: 99%

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Automatic Segmentation of Specific Intervertebral Discs through a Two-Stage MultiResUNet Model

Cheng

Lin

Huang

et al. 2021

JCM

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The automatic segmentation of intervertebral discs from medical images is an important task for an intelligent clinical system. In this study, a deep learning model based on the MultiResUNet model for the automatic segmentation of specific intervertebral discs is presented. MultiResUNet can easily segment all intervertebral discs in MRI images; however, when only certain specific intervertebral discs need to be segmented, problems with segmentation errors, misalignment, and noise occur. In order to solve these problems, a two-stage MultiResUNet model is proposed. Connected-component labeling, automatic cropping, and distance transform are used in the proposed method. The experimental results show that the segmentation errors and misalignments of specific intervertebral discs are greatly reduced, and the segmentation accuracy is increased to about 94%. The performance of the proposed method proves its usefulness for the automatic segmentation of specific intervertebral discs over other deep learning models, such as the U-Net, CNN-based, Attention U-Net, and MultiResUNet models.

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Section: Discussionmentioning

confidence: 99%

“…Ibtehaz et al [ 12 ] proposed the MultiResUNet model to improve on the U-Net model and to segment multimodal medical images. Lou et al [ 13 ] designed a DC-UNet model modified from U-Net and obtained a relative improvement in performance, compared with classical U-Net.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Specific Intervertebral Discs through a Two-Stage MultiResUNet Model

Cheng

Lin

Huang

et al. 2021

JCM

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“…We will compare the segmentation performance of this two‐channel structure with that of the two independent U‐Nets for MAB and LIB segmentations. Compared to the dual‐channel U‐Net (DC‐UNet), 48 which replaces the convolution layers in U‐Net by a two‐stream convolution block called the dual‐channel block, our model uses the convolution layers of U‐Net but has a two‐channel output representing the MAB and LIB segmentation results. Our architecture consists of a single‐stream convolution block with two convolution layers and is more computationally efficient than the dual‐channel block that has two streams, each consisting of three convolution layers.…”

Section: Methodsmentioning

confidence: 99%

Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss

2021

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Purpose Vessel wall volume (VWV) and localized vessel‐wall‐plus‐plaque thickness (VWT) measured from three‐dimensional (3D) ultrasound (US) carotid images are sensitive to anti‐atherosclerotic effects of medical/dietary treatments. VWV and VWT measurements require the lumen‐intima (LIB) and media‐adventitia boundaries (MAB) at the common and internal carotid arteries (CCA and ICA). However, most existing segmentation techniques were capable of segmenting the CCA only. An approach capable of segmenting the MAB and LIB from the CCA and ICA was required to accelerate VWV and VWT quantification. Methods Segmentation for CCA and ICA was performed independently using the proposed two‐channel U‐Net, which was driven by a novel loss function known as the adaptive triple Dice loss (ADTL) function. The training set was augmented by interpolating manual segmentation along the longitudinal direction, thereby taking continuity of the artery into account. A test‐time augmentation (TTA) approach was applied, in which segmentation was performed three times based on the input axial images and its flipped versions; the final segmentation was generated by pixel‐wise majority voting. Results Experiments involving 224 3DUS volumes produce a Dice similarity coefficient (DSC) of 95.1% ± 4.1% and 91.6% ± 6.6% for the MAB and LIB, in the CCA, respectively, and 94.2% ± 3.3% and 89.0% ± 8.1% for the MAB and LIB, in the ICA, respectively. TTA and ATDL independently contributed to a statistically significant improvement to all boundaries except the LIB in ICA. Conclusions The proposed two‐channel U‐Net with ADTL and TTA can segment the CCA and ICA accurately and efficiently from the 3DUS volume. Our approach has the potential to accelerate the transition of 3DUS measurements of carotid atherosclerosis to clinical research.

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“…Other models using ensemble strategies were proposed, in which the best model obtained results of 93.6%, 93.3% and 94.3% of Dice, precision and recall, respectively. This work is very similar to the works of [5,17], but it uses other segmentation models that are gaining prominence in the literature with other medical segmentation datasets [18][19][20][21].…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

Rocha¹,

Endo²

2022

Preprint

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Introduction: Dental segmentation in panoramic radiograph has become very relevant in dentistry, since it allows health professionals to carry out their assessments more clearly and helps them to define the best possible treatment plan for their patients. Objectives: In this work, a comparative study is carried out with four segmentation algorithms (U-Net, DCU-Net, DoubleU-Net and Nano-Net) that are prominent in the medical literature on segmentation and we evaluate their results with the current state of the art of dental segmentation in panoramic radiograph. Methods: These algorithms were tested with a dataset consisting of 1,500 images, considering experiment scenarios with and without augmentation data. Results: DoubleU-Net was the model that presented the best results among the analyzed models, reaching 96.591% accuracy and 92.886% Dice using the dataset with data augmentation. Another model that stood out was Nano-Net using the dataset without data augmentation; this model achieved results close to that of the literature with only 235 thousand trainable parameters, while the literature model (TSASNet) contains 78 million. Conclusions: The results obtained in this work are satisfactory and present paths for a better and more effective dental segmentation process.

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DC-UNet: rethinking the U-Net architecture with dual channel efficient CNN for medical image segmentation

Cited by 91 publications

References 24 publications

Automatic Segmentation of Specific Intervertebral Discs through a Two-Stage MultiResUNet Model

Automatic Segmentation of Specific Intervertebral Discs through a Two-Stage MultiResUNet Model

Segmentation of common and internal carotid arteries from 3D ultrasound images based on adaptive triple loss

A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph

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