Model-Agnostic Post-Processing Based on Recursive Feedback for Medical Image Segmentation

Kim, Jae-Ho; Kang, Seokho

doi:10.1109/access.2021.3130200

Cited by 6 publications

(3 citation statements)

References 46 publications

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“…In medical image processing, deep learning algorithm has been widely used in various tasks. Through deep learning model, different tissues or organs in medical images can be accurately segmented, such as tumor segmentation and brain segmentation [6][7]. The deep learning algorithm is used to detect and diagnose abnormal structures or features in medical images, such as lung nodule detection and diabetic retinopathy recognition.…”

Section: Summary Of Related Workmentioning

confidence: 99%

Optimization and Performance Evaluation of Deep Learning Algorithm in Medical Image Processing

Zhang,

Xiao,

Zhang

et al. 2024

FCIS

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In this paper, the optimization and performance evaluation of deep learning algorithm in medical image processing are studied. Firstly, the paper introduces the importance and challenges of medical image processing, and expounds the application prospect of deep learning in this field. Subsequently, this paper discusses the optimization methods of deep learning algorithm in detail, including model structure design, data preprocessing, super parameter adjustment and so on. In terms of performance evaluation, this study selected classic models such as U-Net, DeepLab and DenseNet, and compared them with ROC curve and AUC value to evaluate their predictive ability in medical image classification. The results show that the DenseNet model shows high performance in prediction accuracy, while the performance of U-Net and DeepLab models is slightly average. Finally, the advantages and disadvantages of each model are analyzed, and the future research direction is prospected. This study is of great significance to promote the development and application of medical image processing technology, and provides important theoretical and technical support for medical diagnosis and treatment.

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Section: Summary Of Related Workmentioning

confidence: 99%

Optimization and Performance Evaluation of Deep Learning Algorithm in Medical Image Processing

Zhang,

Xiao,

Zhang

et al. 2024

FCIS

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“…With Canny, the operator has a more precise and thorough ability to recognise edges [13] . Although the image is processed by an array of morphological operators to reduce the noise interference to some extent, it is inevitable that some small valley bottoms with low gradient will be generated in the gradient image due to some small fluctuations in gray level or quantization errors in the original image, leading to the over-segmentation characteristic in watershed results [14][15][16] .The method used in this study selectively fills some valley bottoms based on the valley bottoms. It reduces the number of valley bottoms in the gradient graph due to the small gradient difference between these "fake" valley bottoms and the surrounding pixels [17] .…”

Section: Image Preprocessingmentioning

confidence: 99%

Medical Image Segmentation Algorithm Based on Markup Improvement Combining the Watershed Algorithm and the Level Set Algorithm

2024

AJCIS

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In this paper, a method is proposed to merge the level set segmentation and the watershed segmentation for edge detection in medical images. To solve the issue of over-segmentation in the watershed algorithm, this improved watershed algorithm is proposed. By identifying the foreground and background, the number of segmented zones can be adjusted. For the process of filling the valley bottom, the divided region's contour is enhanced and modified to ensure that it fits as closely as possible to the target edge. The location of the edge in the gradient map is enhanced and adjusted, while the largest connected region is applied to acquire image edge information. Experiments have shown that this technique can reduce over-segmentation in medical images.

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“…To enhance model attention on critical regions, an attention mechanism is introduced, dynamically adjusting feature importance across different positions. We propose a self-attention mechanism, dubbed "attention U-Net," facilitating automatic learning of feature dependencies and guiding the model's focus towards task-specific key areas [15].…”

Section: Model Designmentioning

confidence: 99%

Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction

Zi,

Wang,

Gao

et al. 2024

AJST

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Medical image segmentation (MIS) and 3D reconstruction are crucial research directions in the field of medical imaging, which is of great significance for disease diagnosis, treatment planning and surgical navigation. In recent years, with the rapid development of Deep Learning (DL) technology, DL has made remarkable progress in the field of medical image processing and has become one of the important methods of MIS and 3D reconstruction. In this paper, the application of DL technologies in MIS and 3D reconstruction is systematically studied and discussed. Firstly, the paper introduces the basic concepts and technical challenges of MIS and 3D reconstruction, including image quality, noise interference and edge detection. Secondly, the paper introduces the data acquisition process in detail, including the medical image data set and data preprocessing method. Then, the paper puts forward the DL model framework based on self-attention mechanism, as well as the loss function and optimizer used in the training process. Then, the model is verified by experiments, and the performance of different models in MIS and 3D reconstruction is analyzed. Finally, the experimental results are comprehensively analyzed, and the application prospect and future development direction of DL in MIS and 3D reconstruction are discussed. The research results of this paper provide important theoretical and practical guidance for improving medical image processing technology and promoting the development and clinical application of medical imaging.

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Model-Agnostic Post-Processing Based on Recursive Feedback for Medical Image Segmentation

Cited by 6 publications

References 46 publications

Optimization and Performance Evaluation of Deep Learning Algorithm in Medical Image Processing

Optimization and Performance Evaluation of Deep Learning Algorithm in Medical Image Processing

Medical Image Segmentation Algorithm Based on Markup Improvement Combining the Watershed Algorithm and the Level Set Algorithm

Research on the Application of Deep Learning in Medical Image Segmentation and 3D Reconstruction

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