OAU-net: Outlined Attention U-net for biomedical image segmentation

Song, Hang; Wang, Yuefei; Zeng, Shijie; Guo, Xiaojun; Li, Zheheng

doi:10.1016/j.bspc.2022.104038

Cited by 16 publications

(5 citation statements)

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“…For the LASC dataset, the ensemble model achieves the highest performance (Table 5). According to Table 6, nnU-Net demonstrates competitive performance with other methods, with only [34] surpassing nnU-Net by 0.1%. Interestingly, even the nnU-Net (2D) model shows competitive performance compared to the latest models [36].…”

Section: Lascmentioning

confidence: 99%

“…As in the ACDC dataset, MnM performance is evaluated on both ES (Table 10) and ED (Table 11) phases. The 2D nnU-Net outperforms both 3D and ensemble models in 0.890 17.124 1.706 [28] 0.866 -- [24] 0.890 16.450 1.715 [29] 0.878 -0.710 [30] 0.846 105.700 3.390 [31] 0.872 22.394 - [32] 0.875 24.731 2.233 [14] 0.881 18.755 1.782 [33] 0.883 20.883 1.794 [11] 0.890 16.907 1.720 [23] 0.893 15.860 1.613 [13] 0.886 18.389 1.813 [34] 0.929 12.960 0.890 [35] 0.919 RV segmentation in the ES phase, while the 3D full-resolution model also outperforms LV segmentation in terms of dice score in the ES phase. In the ED phase, the ensemble model demonstrates superior performance.…”

Section: Mnmmentioning

confidence: 99%

“…Publication DSC [38] 0.932 [39] 0.926 [40] 0.925 [10] 0.925 [41] 0.923 [42] 0.920 [43] 0.932 [44] 0.919 [45] 0.903 [46] 0.898 [47] 0.923 [48] 0.920 [49] 0.913 [50] 0.923 [51] 0.921 [52] 0.911 [34] 0.935 [35] 0.934 [53] 0.919 [54] 0.910 [55] 0.918 [56] 0.920 [36] 0.926…”

Section: Dsc-dice Scorementioning

confidence: 99%

See 2 more Smart Citations

How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation

Gunawardhana,

Xu,

Zhao

2024

Preprint

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Cardiac segmentation is a critical task in medical imaging, essential for detailed analysis of heart structures, which is crucial for diagnosing and treating various cardiovascular diseases. With the advent of deep learning, automated segmentation techniques have demonstrated remarkable progress, achieving high accuracy and efficiency compared to traditional manual methods. Among these techniques, the nnU-Net framework stands out as a robust and versatile tool for medical image segmentation. In this study, we evaluate the performance of nnU-Net in segmenting cardiac magnetic resonance images (MRIs). Utilizing five cardiac segmentation datasets, we employ various nnU-Net configurations, including 2D, 3D full resolution, 3D low resolution, 3D cascade, and ensemble models. Our study benchmarks the capabilities of these configurations and examines the necessity of developing new models for specific cardiac segmentation tasks.

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Section: Lascmentioning

confidence: 99%

Section: Mnmmentioning

confidence: 99%

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How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation

Gunawardhana,

Xu,

Zhao

2024

Preprint

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“…Next we compare the CTV delineation quality of several DL models, including the proposed PPAF-net, PPF-net, the RA-CTVNet, 23 model by Rhee et al, 20 U-Net, 24 U-Net++, 25 EANet, 44 DeepLab v3+, 22 MSRF-Net, 45 TransUNet, 46 SegFormer, 47 SETR, 48 PA-Net 49 and OAU-Net. 50 The PPF-net is the PPAF-net without the attention module. Performance metrics are DSC, SEN, PPV, F1-score, and HD.…”

Section: Performance Evaluationmentioning

confidence: 99%

Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks

Tian,

Wang,

Liu

et al. 2023

Medical Physics

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PurposeDelineation of the clinical target volume (CTV) and organs‐at‐risk (OARs) is important in cervical cancer radiotherapy. But it is generally labor‐intensive, time‐consuming, and subjective. This paper proposes a parallel‐path attention fusion network (PPAF‐net) to overcome these disadvantages in the delineation task.MethodsThe PPAF‐net utilizes both the texture and structure information of CTV and OARs by employing a U‐Net network to capture the high‐level texture information, and an up‐sampling and down‐sampling (USDS) network to capture the low‐level structure information to accentuate the boundaries of CTV and OARs. Multi‐level features extracted from both networks are then fused together through an attention module to generate the delineation result.ResultsThe dataset contains 276 computed tomography (CT) scans of patients with cervical cancer of staging IB‐IIA. The images are provided by the West China Hospital of Sichuan University. Simulation results demonstrate that PPAF‐net performs favorably on the delineation of the CTV and OARs (e.g., rectum, bladder and etc.) and achieves the state‐of‐the‐art delineation accuracy, respectively, for the CTV and OARs. In terms of the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD), 88.61% and 2.25 cm for the CTV, 92.27% and 0.73 cm for the rectum, 96.74% and 0.68 cm for the bladder, 96.38% and 0.65 cm for the left kidney, 96.79% and 0.63 cm for the right kidney, 93.42% and 0.52 cm for the left femoral head, 93.69% and 0.51 cm for the right femoral head, 87.53% and 1.07 cm for the small intestine, and 91.50% and 0.84 cm for the spinal cord.ConclusionsThe proposed automatic delineation network PPAF‐net performs well on CTV and OARs segmentation tasks, which has great potential for reducing the burden of radiation oncologists and increasing the accuracy of delineation. In future, radiation oncologists from the West China Hospital of Sichuan University will further evaluate the results of network delineation, making this method helpful in clinical practice.

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“…The decoder layers, on the other hand, focus on generating a finer segmentation result by jointly considering semantic information and spatial information. The skip connection enables the decoder layers to jointly process the feature maps from both deeper and shallower encoder layers so as to capture image features at different scales and depths and produce more accurate segmentation results [25,26]. When segmenting the IAN canals, UNet is also the most often adopted architecture and has presented good accuracy in both institutional datasets [8][9][10][11] and public datasets [13].…”

Section: Introductionmentioning

confidence: 99%

Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions

Liu,

Yang,

Zhang

et al. 2024

Bioengineering

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Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve 75.55% and 81.35% in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a 2.39% and 2.82% gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts.

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OAU-net: Outlined Attention U-net for biomedical image segmentation

Cited by 16 publications

References 16 publications

How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation

How good nnU-Net for Segmenting Cardiac MRI: A Comprehensive Evaluation

Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks

Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions

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