Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation

Li, Chen; Chen, Wei; Tan, Yusong

doi:10.3390/app10186439

Cited by 6 publications

(4 citation statements)

References 36 publications

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“…F1-score is a measure widely accepted and used in publications for the evaluation of the possibilities of the detection and segmentation of medical images. Also, the Hausdorff metric (d H ) was calculated for each pair of expert and system-generated masks (4).…”

Section: Resultsmentioning

confidence: 99%

“…The development of the new GPU graphics cards has contributed to the possibility of wide application in prediction and classification tasks in a wide variety of scientific and industrial fields. Today, neural networks are the subject of intense research, especially in the field of medical image processing [2][3][4][5]. The U-Net was designed for medical image processing in the Computer Science Department at the University of Freiburg, Germany [6].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bulletin of the Polish Academy of Sciences: Technical Sciences

Les

2021

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This work presents an automatic system for generating kidney boundaries in computed tomography (CT) images. This paper presents the main points of medical image processing, which are the parts of the developed system. The U-Net network was used for image segmentation, which is now widely used as a standard solution for many medical image processing tasks. An innovative solution for framing the input data has been implemented to improve the quality of the learning data as well as to reduce the size of the data. Precision-recall analysis was performed to calculate the optimal image threshold value. To eliminate false-positive errors, which are a common issue in segmentation based on neural networks, the volumetric analysis of coherent areas was applied. The developed system facilitates a fully automatic generation of kidney boundaries as well as the generation of a three-dimensional kidney model. The system can be helpful for people who deal with the analysis of medical images, medical specialists in medical centers, especially for those who perform the descriptions of CT examination. The system works fully automatically and can help to increase the accuracy of the performed medical diagnosis and reduce the time of preparing medical descriptions.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Les

2021

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“…A whole group of distinct methods uses machine learning techniques. Among these solutions, one can find solutions based on U-net neural networks [12][13][14][15] which are very often used in medical image segmentation tasks.…”

Section: Methods Of Automatic Renal Localizationmentioning

confidence: 99%

Kidney Boundary Detection Algorithm Based on Extended Maxima Transformations for Computed Tomography Diagnosis

et al. 2020

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This article describes the automated computed tomography (CT) image processing technique supporting kidney detection. The main goal of the study is a fully automatic generation of a kidney boundary for each slice in the set of slices obtained in the computed tomography examination. This work describes three main tasks in the process of automatic kidney identification: the initial location of the kidneys using the U-Net convolutional neural network, the generation of an accurate kidney boundary using extended maxima transformation, and the application of the slice scanning algorithm supporting the process of generating the result for the next slice, using the result of the previous one. To assess the quality of the proposed technique of medical image analysis, automatic numerical tests were performed. In the test section, we presented numerical results, calculating the F1-score of kidney boundary detection by an automatic system, compared to the kidneys boundaries manually generated by a human expert from a medical center. The influence of the use of U-Net support in the initial detection of the kidney on the final F1-score of generating the kidney outline was also evaluated. The F1-score achieved by the automated system is 84% ± 10% for the system without U-Net support and 89% ± 9% for the system with U-Net support. Performance tests show that the presented technique can generate the kidney boundary up to 3 times faster than raw U-Net-based network. The proposed kidney recognition system can be successfully used in systems that require a very fast image processing time. The measurable effect of the developed techniques is a practical help for doctors, specialists from medical centers dealing with the analysis and description of medical image data.

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“…However, the above datadriven methods inevitably require a large amount of labeled data [127,128]. However, labeling medical images is time-consuming and labor-intensive, which also requires specific professional knowledge [129,130]. Therefore, it is efficient to use active learning to select samples that are difficult to predict by the model for selective labeling.…”

Section: Intelligent Medical Assisted Diagnosismentioning

confidence: 99%

Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges

Yao

2022

Applied Sciences

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Active learning is a label-efficient machine learning method that actively selects the most valuable unlabeled samples to annotate. Active learning focuses on achieving the best possible performance while using as few, high-quality sample annotations as possible. Recently, active learning achieved promotion combined with deep learning-based methods, which are named deep active learning methods in this paper. Deep active learning plays a crucial role in computer vision tasks, especially in label-insensitive scenarios, such as hard-to-label tasks (medical images analysis) and time-consuming tasks (autonomous driving). However, deep active learning still has some challenges, such as unstable performance and dirty data, which are future research trends. Compared with other reviews on deep active learning, our work introduced the deep active learning from computer vision-related methodologies and corresponding applications. The expected audience of this vision-friendly survey are researchers who are working in computer vision but willing to utilize deep active learning methods to solve vision problems. Specifically, this review systematically focuses on the details of methods, applications, and challenges in vision tasks, and we also introduce the classic theories, strategies, and scenarios of active learning in brief.

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Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation

Cited by 6 publications

References 36 publications

Bulletin of the Polish Academy of Sciences: Technical Sciences

Bulletin of the Polish Academy of Sciences: Technical Sciences

Kidney Boundary Detection Algorithm Based on Extended Maxima Transformations for Computed Tomography Diagnosis

Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges

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