A Bypass-Based U-Net for Medical Image Segmentation

Chen, Kaixuan; Xu, Geng-Xin; Qian, Jing; Ren, Chuan-Xian

doi:10.1007/978-3-030-36189-1_13

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…Model/Methods used [102], [182]- [190] Base U-net [34]- [36], [191] Attention gate [55], [169], [192], [193] Residual block [70] Dense block [38] Adversarial net; GAN; Attention gate [91] Cascaded U-net [194] Attention gate; Residual block [45] Dense block; Inception block [195] Inception block; Residual block [97] Modified U-net with parallel decoders [196] Recurrent residual block; Up skip connections…”

Section: Referencementioning

confidence: 99%

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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a 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 nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: Referencementioning

confidence: 99%

“…Base U-net [39] Attention gate [59] Residual block [70] Dense block [194] Attention gate; Residual block [196] Up skip connections Psoriasis [209] Base U-net…”

Section: Melanoma [210]-[218]mentioning

confidence: 99%

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…The results of Table 3 show that the Precision of this method was less than 0.84 at the grain filling and maturity stages, and the four evaluation indexes were less than 0.84 in other areas, which was the worst among the four deep learning networks. The Unet network can improve the encoding and decoding form, the relative character of the coding part is retained by connecting the encoding and decoding features (Chen et al, 2019). Compared with the Segnet network, the accuracy was improved, the Precision, Dice, Recall and Accuracy were all higher than 0.85 at the filling and mature stages.…”

Section: The Difference Between the Proposed Methods And Other Classi...mentioning

confidence: 99%

Wheat lodging extraction using Improved_Unet network

Cheng

Cai

et al. 2022

Front. Plant Sci.

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The accurate extraction of wheat lodging areas can provide important technical support for post-disaster yield loss assessment and lodging-resistant wheat breeding. At present, wheat lodging assessment is facing the contradiction between timeliness and accuracy, and there is also a lack of effective lodging extraction methods. This study aims to propose a wheat lodging assessment method applicable to multiple Unmanned Aerial Vehicle (UAV) flight heights. The quadrotor UAV was used to collect high-definition images of wheat canopy at the grain filling and maturity stages, and the Unet network was evaluated and improved by introducing the Involution operator and Dense block module. The performance of the Improved_Unet was determined using the data collected from different flight heights, and the robustness of the improved network was verified with data from different years in two different geographical locations. The results of analyses show that (1) the Improved_Unet network was better than other networks (Segnet, Unet and DeeplabV3+ networks) evaluated in terms of segmentation accuracy, with the average improvement of each indicator being 3% and the maximum average improvement being 6%. The Improved_Unet network was more effective in extracting wheat lodging areas at the maturity stage. The four evaluation indicators, Precision, Dice, Recall, and Accuracy, were all the highest, which were 0.907, 0.929, 0.884, and 0.933, respectively; (2) the Improved_Unet network had the strongest robustness, and its Precision, Dice, Recall, and Accuracy reached 0.851, 0.892, 0.844, and 0.885, respectively, at the verification stage of using lodging data from other wheat production areas; and (3) the flight height had an influence on the lodging segmentation accuracy. The results of verification show that the 20-m flight height performed the best among the flight heights of 20, 40, 80 and 120 m evaluated, and the segmentation accuracy decreased with the increase of the flight height. The Precision, Dice, Recall, and Accuracy of the Improved_Unet changed from 0.907 to 0.845, from 0.929 to 0.864, from 0.884 to 0.841, and from 0.933 to 0.881, respectively. The results demonstrate the improved ability of the Improved-Unet to extract wheat lodging features. The proposed deep learning network can effectively extract the areas of wheat lodging, and the different height fusion models developed from this study can provide a more comprehensive reference for the automatic extraction of wheat lodging.

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Research on Arc Sag Measurement Methods for Transmission Lines Based on Deep Learning and Photogrammetry Technology

et al. 2023

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Arc sag is an important parameter in the design and operation and maintenance of transmission lines and is directly related to the safety and reliability of grid operation. The current arc sag measurement method is inefficient and costly, which makes it difficult to meet the engineering demand for fast inspection of transmission lines. In view of this, this paper proposes an automatic spacer bar segmentation algorithm, CM-Mask-RCNN, that combines the CAB attention mechanism and MHSA self-attention mechanism, which automatically extracts the spacer bars and calculates the center coordinates, and combines classical algorithms such as beam method leveling, spatial front rendezvous, and spatial curve fitting, based on UAV inspection video data, to realize arc sag measurement with a low cost and high efficiency. It is experimentally verified that the CM-Mask-RCNN algorithm proposed in this paper achieves an AP index of 73.40% on the self-built dataset, which is better than the Yolact++, U-net, and Mask-RCNN algorithms. In addition, it is also verified that the adopted approach of fusing CAB and MHSA attention mechanisms can effectively improve the segmentation performance of the model, and this combination improves the model performance more significantly compared with other attention mechanisms, with an AP improvement of 2.24%. The algorithm in this paper was used to perform arc sag measurement experiments on 10 different transmission lines, and the measurement errors are all within ±2.5%, with an average error of −0.11, which verifies the effectiveness of the arc sag measurement method proposed in this paper for transmission lines.

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A Bypass-Based U-Net for Medical Image Segmentation

Cited by 3 publications

References 11 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Wheat lodging extraction using Improved_Unet network

Research on Arc Sag Measurement Methods for Transmission Lines Based on Deep Learning and Photogrammetry Technology

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