首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography
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Adrenal Tumor Vessels Segmentation Using Convolutional Neural Network in Computed Tomography Angiography

机译:在计算机断层扫描血管造影中使用卷积神经网络对肾上腺肿瘤血管进行分割

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The adrenal glands are important endocrine glands in humans. They are in complex environments with thin vessels around them. It’s meaningful to get the accurate dissection before surgery. However, images used in hospitals are now unable to help doctors with many surgeries, which are produced by digital subtraction angiography. In this study, we used a 3D U-Net model to segment the adrenal tumor vessels in 3D computed tomography angiography slices. The model was evaluated by dice similarity coefficient (DSC) and mean intersection over union (MIoU) with the manually labeled ground truth. The DSC in this model is 94.69% and the MIoU is 90.22%.
机译:肾上腺是人类重要的内分泌腺。它们处于复杂的环境中,周围围绕着细小的​​血管。在手术前进行准确的解剖很有意义。但是,医院中使用的图像现在无法通过数字减影血管造影术为许多外科手术医生提供帮助。在这项研究中,我们使用3D U-Net模型在3D计算机断层扫描血管造影切片中分割肾上腺肿瘤血管。通过骰子相似系数(DSC)和联合平均交集(MIoU)与手动标记的地面真相对模型进行评估。该模型中的DSC为94.69%,MIoU为90.22%。

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