Abstract：
UNASSIGNED: The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment.
UNASSIGNED: To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images.
UNASSIGNED: The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation.
UNASSIGNED: Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods.
UNASSIGNED: The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients\' conditions and making informed treatment decisions.
UNASSIGNED: To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images.
UNASSIGNED: The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation.
UNASSIGNED: Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods.
UNASSIGNED: The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients\' conditions and making informed treatment decisions.
摘要：
■肾脏肿瘤的发病率逐年递增。肾脏肿瘤的分割精度对诊断和治疗至关重要。
■为了提高准确性并减少人工参与，提出了一种基于深度学习的CT图像中肾脏和肾脏肿瘤自动分割方法。
■所提出的方法包括两个部分：对象检测和分割。我们首先使用一个模型来检测肾脏的位置，然后缩小分割范围，最后使用注意递归残差卷积网络进行分割。
■我们的模型在KiTS19数据集上获得了0.951的肾脏骰子得分和0.895的肿瘤骰子得分。实验结果表明，我们的模型显着提高了肾脏和肾脏肿瘤分割的准确性，并且优于其他高级方法。
■所提出的方法为在CT图像上准确分割肾脏和肾脏肿瘤提供了一种高效且自动的解决方案。此外,这项研究可以帮助放射科医师评估患者病情并做出明智的治疗决定.
■为了提高准确性并减少人工参与，提出了一种基于深度学习的CT图像中肾脏和肾脏肿瘤自动分割方法。
■所提出的方法包括两个部分：对象检测和分割。我们首先使用一个模型来检测肾脏的位置，然后缩小分割范围，最后使用注意递归残差卷积网络进行分割。
■我们的模型在KiTS19数据集上获得了0.951的肾脏骰子得分和0.895的肿瘤骰子得分。实验结果表明，我们的模型显着提高了肾脏和肾脏肿瘤分割的准确性，并且优于其他高级方法。
■所提出的方法为在CT图像上准确分割肾脏和肾脏肿瘤提供了一种高效且自动的解决方案。此外,这项研究可以帮助放射科医师评估患者病情并做出明智的治疗决定.
