FAU-Net: Fixup Initialization Channel Attention Neural Network for Complex Blood Vessel Segmentation

Huang, Daren; Yin, Liwen; Guo, Hao; Tang, Wen; Wan, Tao

doi:10.3390/app10186280

Cited by 5 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The weight initialization problem had been discussed before the inception of BatchNorm [5]. In 2019, a paper based on research done at Facebook developed a method called Fixup Initialization [28]. This method is another attempt to solve the exploding and vanishing gradient issue, which is related to the fact that the deeper the neural net, the larger the variance of its output will tend to be.…”

Section: Residual Learning Without Normalizationmentioning

confidence: 99%

An Empirical Analysis of the Shift and Scale Parameters in BatchNorm

Peerthum¹,

Stamp²

2023

Preprint

View full text Add to dashboard Cite

Batch Normalization (BatchNorm) is a technique that improves the training of deep neural networks, especially Convolutional Neural Networks (CNN). It has been empirically demonstrated that BatchNorm increases performance, stability, and accuracy, although the reasons for such improvements are unclear. BatchNorm includes a normalization step as well as trainable shift and scale parameters. In this paper, we empirically examine the relative contribution to the success of BatchNorm of the normalization step, as compared to the re-parameterization via shifting and scaling. To conduct our experiments, we implement two new optimizers in PyTorch, namely, a version of BatchNorm that we refer to as AffineLayer, which includes the re-parameterization step without normalization, and a version with just the normalization step, that we call BatchNorm-minus. We compare the performance of our AffineLayer and BatchNorm-minus implementations to standard BatchNorm, and we also compare these to the case where no batch normalization is used. We experiment with four ResNet architectures (ResNet18, ResNet34, ResNet50, and ResNet101) over a standard image dataset and multiple batch sizes. Among other findings, we provide empirical evidence that the success of BatchNorm may derive primarily from improved weight initialization.

show abstract

Section: Residual Learning Without Normalizationmentioning

confidence: 99%

An Empirical Analysis of the Shift and Scale Parameters in BatchNorm

Peerthum¹,

Stamp²

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It is popular because it is easily interpretable and allows comparisons with other studies [ 14 ]. Less often, other performance measures such as the average Hausdorff distance [ 15 ], the area under the receiver operating characteristic curve [ 16 ], sensitivity [ 17 , 18 ], specificity [ 18 ], or accuracy [ 16 – 18 ] are used.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of performance measures for arterial brain vessel segmentation

et al. 2021

View full text Add to dashboard Cite

Background Arterial brain vessel segmentation allows utilising clinically relevant information contained within the cerebral vascular tree. Currently, however, no standardised performance measure is available to evaluate the quality of cerebral vessel segmentations. Thus, we developed a performance measure selection framework based on manual visual scoring of simulated segmentation variations to find the most suitable measure for cerebral vessel segmentation. Methods To simulate segmentation variations, we manually created non-overlapping segmentation errors common in magnetic resonance angiography cerebral vessel segmentation. In 10 patients, we generated a set of approximately 300 simulated segmentation variations for each ground truth image. Each segmentation was visually scored based on a predefined scoring system and segmentations were ranked based on 22 performance measures common in the literature. The correlation of visual scores with performance measure rankings was calculated using the Spearman correlation coefficient. Results The distance-based performance measures balanced average Hausdorff distance (rank = 1) and average Hausdorff distance (rank = 2) provided the segmentation rankings with the highest average correlation with manual rankings. They were followed by overlap-based measures such as Dice coefficient (rank = 7), a standard performance measure in medical image segmentation. Conclusions Average Hausdorff distance-based measures should be used as a standard performance measure in evaluating cerebral vessel segmentation quality. They can identify more relevant segmentation errors, especially in high-quality segmentations. Our findings have the potential to accelerate the validation and development of novel vessel segmentation approaches.

show abstract

Deep learning technology in vascular image segmentation and disease diagnosis

Du,

Zhuang,

Huang

2024

Journal of Intelligent Medicine

View full text Add to dashboard Cite

Blood vessel segmentation is a crucial aspect of medical image processing, aiding medical professionals in more accurate disease analysis and diagnosis. Manual blood vessel segmentation methods are time‐consuming and cumbersome, making the development of automatic segmentation methods essential. The rapid advancements in deep learning technology have introduced new tools and methods for vascular image segmentation. In this review, we provide a comprehensive overview of deep learning‐based blood vessel segmentation methods across various fields, including retinal vessel segmentation, cerebrovascular segmentation, and pulmonary vessel segmentation. Several prevalent diseases, such as retinal vascular diseases, cerebrovascular diseases, pulmonary vascular diseases, and tumors, have posed significant health challenges globally. This review also discusses the application of deep learning technology in disease diagnosis within these contexts. Finally, considering the current research landscape, we discuss existing challenges and potential future developments in blood vessel segmentation. We aim to assist researchers in gaining a comprehensive understanding and designing effective blood vessel segmentation models, ultimately offering opportunities for early disease diagnosis and treatment.

show abstract

FAU-Net: Fixup Initialization Channel Attention Neural Network for Complex Blood Vessel Segmentation

Cited by 5 publications

References 23 publications

An Empirical Analysis of the Shift and Scale Parameters in BatchNorm

An Empirical Analysis of the Shift and Scale Parameters in BatchNorm

An evaluation of performance measures for arterial brain vessel segmentation

Deep learning technology in vascular image segmentation and disease diagnosis

Contact Info

Product

Resources

About