双态形状重构及其在前列腺超声图像分割中的应用

前列腺超声图像在临床中的准确分割对于后续诊断具有重要的影响，而当前已有研究结论无法精确分割各个部分。提出了一种基于点分布模型和流形学习的双态形状重构的方法，并对前列腺超声图像进行分割：通过随机森林指示隐态表达进行目标初定位；改进边界算子以改善粗分割准确性；使用显态表达与噪声部分相邻的部分灰度显著点来进行插值计算，从而恢复整体形状。该分割方式不仅减少了数据计算量，还增加了分割可靠性。实验表明，该方法的DSC指标为97.38%,mIoU指标为95.24%,精度强于当前热门分割神经网络。

Dual-state shape reconstitution and its application in trus segmentation

Accurate segmentation of prostate ultrasound images in the clinic has an important impact on the subsequent diagnosis, and the currently available research findings cannot accurately segment each part. This paper proposed a bimodal shape reconstruction method based on the point distribution model and stream shape learning and segmentation of prostate ultrasound images: target initial localization by random forest indication of the hidden state expression, improvement of the boundary operator to improve the coarse segmentation accuracy, interpolation computation using some gray significant points adjacent to the noisy part of the explicit state expression to recover the shape as a whole. This segmentation method not only reduced the data computation amount, but increased the segmentation reliability. Experiments show that the DSC of the proposed method is 97.38% and the mIoU is 95.24%, and the accuracy is stronger than the current popular segmentation neural networks.