In the treatment of male anterior urethral stricture, three-dimensional digital urethral model (TDUM) can be tissue replacement for repairing urethral stricture. The segmentation of corpus spongiosum is key to reconstructing TDUM. In this paper, we extracted Gabor features of ultrasound images and selected different parameters of neural network model for the segmentation of male anterior urethra corpus spongiosum. Two group experiments were conducted: part urethra dataset (PUD) experiment and full urethra dataset (FUD) experiment. In PUD experiment, different parameters of neural network model were selected to obtain the model with best performance. The best performance model was applied in FUD experiment, in which bulbous urethra (BU), penile urethra (PU), bulbar urethral stricture (BUS) and penile urethral stricture (PUS) images were tested respectively. The experimental result shows that our method is robust in segmentation of corpus spongiosum in urethral ultrasound images, and the segmentation accuracy of corpus spongiosum is over 90%. The performance was recognized by clinical ultrasound physicians and urologists. Combined with tissue engineering and 3D printing technology, this method makes the TDUM possible which then provides a new alternative for the treatment of the complex urethral strictures.
展开▼
机译：在治疗雄性前尿道狭窄中，三维数字尿道模型（TDUM）可以是组织替代修复尿道狭窄。 Corpus Spongiosum的分割是重建TDUM的关键。本文提取了超声图像的Gabor特征，并为雄性前尿道的分割进行了神经网络模型的选择不同参数。进行两组实验：部分尿道数据集（PUD）实验和全尿道数据集（FUD）实验。在PUD实验中，选择了神经网络模型的不同参数，以获得最佳性能的模型。在FUD实验中应用了最佳性能模型，其中分别进行了球形尿道（BU），阴茎尿道（PU），BULBAR尿道狭窄（公共汽车）和阴茎尿道缩小（PUS）图像。实验结果表明，我们的方法在尿道超声图像中的语料皮层分割中是稳健的，并且Corpus Spongosum的分割精度超过90％。临床超声医生和泌尿科医生认可的表现。结合组织工程和3D印刷技术，该方法使得TDUM可以为其提供复杂尿道狭窄的治疗提供新的替代方案。
展开▼
