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Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient

机译：使用卷积神经网络深度学习框架基于蒙版区域进行图像分割：更准确更便捷

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BackgroundBurns are life-threatening with high morbidity and mortality. Reliable diagnosis supported by accurate burn area and depth assessment is critical to the success of the treatment decision and, in some cases, can save the patient’s life. Current techniques such as straight-ruler method, aseptic film trimming method, and digital camera photography method are not repeatable and comparable, which lead to a great difference in the judgment of burn wounds and impede the establishment of the same evaluation criteria. Hence, in order to semi-automate the burn diagnosis process, reduce the impact of human error, and improve the accuracy of burn diagnosis, we include the deep learning technology into the diagnosis of burns.

机译：背景烧伤威胁生命，发病率和死亡率高。可靠的诊断以及准确的烧伤面积和深度评估对决定治疗的成功至关重要，在某些情况下，可以挽救患者的生命。直尺法，无菌胶片修整法和数码相机摄影法等现有技术不可重复且可比，从而导致烧伤创口的判断存在很大差异，并阻碍了建立相同的评估标准。因此，为了使烧伤诊断过程半自动化，减少人为错误的影响，并提高烧伤诊断的准确性，我们将深度学习技术纳入烧伤诊断中。

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期刊名称 Burns Trauma
作者
Chong Jiao; Kehua Su; Weiguo Xie; Ziqing Ye;
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作者单位

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年(卷),期 2019(7),-1
年度 2019
页码 6
总页数 14
原文格式 PDF
正文语种
中图分类外科学;
关键词
Burn image Deep learning Mask R-CNN Image segmentation;

机译：刻录图像;深度学习;Mask R-CNN;图像分割;

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1. Image segmentation of underfloor scenes using a mask regions convolutional neural network with two-stage transfer learning [J] . Automation in construction . 2020,第May期

机译：使用两阶段转移学习的遮罩区域卷积神经网络对地板场景进行图像分割
2. MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning [J] . Dominik Müller, Frank Kramer BMC Medical Imaging . 2021,第1期

机译：MISCNN：卷积神经网络和深度学习的医学图像分割框架
3. Segmentation of retinal fluid based on deep learning:application of three-dimensional fully convolutional neural networks in optical coherence tomography images [J] . Meng-Xiao Li1, Su-Qin Yu2, Wei Zhang1, 国际眼科杂志：英文版 . 2019,第006期

机译：基于深度学习的视网膜液分割：三维全卷积神经网络在光学相干断层扫描图像中的应用
4. Automated detection of macrophages in quantitative phase images by deep learning using a Mask Region-based Convolutional Neural Network [C] . Kai Eder, Tobias Kutscher, Anne Marzi, Conference on Label-free Biomedical Imaging and Sensing . 2021

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5. Plant Segmentation by Supervised Machine Learning Methods and Phenotypic Trait Extraction of Soybean Plants Using Deep Convolutional Neural Networks with Transfer Learning [D] . Adams, Jason R. 2020

机译：植物分割通过深度卷积神经网络与转移学习的豆豆植物的植物分割和表型特性
6. MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning [O] . Dominik Müller, Frank Kramer 2021

机译：MISCNN：卷积神经网络和深度学习的医学图像分割框架
7. Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient [O] . Chong Jiao, Kehua Su, Weiguo Xie, 2019

机译：基于掩模区域的卷积性神经网络深度学习框架刻录图像分割：更准确，更方便

Burn image segmentation based on Mask Regions with Convolutional Neural Network deep learning framework: more accurate and more convenient

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