Transformer-based multilevel region and edge aggregation network for magnetic resonance image segmentation

Chen, Shaolong; Zhong, Lijie; Qiu, Changzhen; Zhang, Zhiyong; Zhang, Xiaodong

doi:10.1016/j.compbiomed.2022.106427

Cited by 7 publications

(2 citation statements)

References 34 publications

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“…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%

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: Dsc-dice Scorementioning

confidence: 99%

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

Gunawardhana,

Xu,

Zhao

2024

Preprint

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“…Chen et al. [ 48 ] introduced a transformer-based method that incorporates multilevel region and edge information and thus achieved high DSI scores on their magnetic resonance image test dataset. To further improve the run-time quality and segmentation performance of such methods, Uslu and Bharath [ 49 ] proposed a quality control method that ultimately aims to increase the trustworthiness of DCNN-based methods in the medical image analysis field.…”

Section: Related Workmentioning

confidence: 99%

A Two-Stage Automatic Color Thresholding Technique

Shamna

Ellam²,

Grübl

et al. 2023

Sensors

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Thresholding is a prerequisite for many computer vision algorithms. By suppressing the background in an image, one can remove unnecessary information and shift one’s focus to the object of inspection. We propose a two-stage histogram-based background suppression technique based on the chromaticity of the image pixels. The method is unsupervised, fully automated, and does not need any training or ground-truth data. The performance of the proposed method was evaluated using a printed circuit assembly (PCA) board dataset and the University of Waterloo skin cancer dataset. Accurately performing background suppression in PCA boards facilitates the inspection of digital images with small objects of interest, such as text or microcontrollers on a PCA board. The segmentation of skin cancer lesions will help doctors to automate skin cancer detection. The results showed a clear and robust background–foreground separation across various sample images under different camera or lighting conditions, which the naked implementation of existing state-of-the-art thresholding methods could not achieve.

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