论文标题
升级的W-NET带有注意门及其在无监督的3D肝分段中的应用
Upgraded W-Net with Attention Gates and its Application in Unsupervised 3D Liver Segmentation
论文作者
论文摘要
生物医学图像的分割可以帮助放射学家通过帮助检测异常（例如肿瘤）来更快地做出更好的诊断并做出决定。但是，手动或半自动分段可能是一项耗时的任务。大多数基于深度学习的自动分割方法受到监督，并依靠手动分割的基础真相。解决该问题的可能解决方案是一种无监督的基于深度学习的自动细分方法，这项研究工作试图解决该方法。我们使用W-NET体系结构并修改它，以便将其应用于3D卷。此外，为了抑制分割中的噪声，我们在跳过连接中增加了注意门。使用软n切割和使用SSIM的重建输出计算分割输出的损失。有条件的随机场被用作处理结果的后处理步骤。提出的方法显示出令人鼓舞的结果，与手动分割相比，肝脏分割的骰子系数为0.88。
Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmented ground-truth. A possible solution for the problem would be an unsupervised deep learning based approach for automated segmentation, which this research work tries to address. We use a W-Net architecture and modified it, such that it can be applied to 3D volumes. In addition, to suppress noise in the segmentation we added attention gates to the skip connections. The loss for the segmentation output was calculated using soft N-Cuts and for the reconstruction output using SSIM. Conditional Random Fields were used as a post-processing step to fine-tune the results. The proposed method has shown promising results, with a dice coefficient of 0.88 for the liver segmentation compared against manual segmentation.
