Vikal Siddharth, Haker Steven, Tempany Clare, Fichtinger Gabor
School of Computing, Queen's University, Kingston, ON, Canada.
Proc SPIE Int Soc Opt Eng. 2009 Mar 27;7259:72594A. doi: 10.1117/12.812433.
With MRI possibly becoming a modality of choice for detection and staging of prostate cancer, fast and accurate outlining of the prostate is required in the volume of clinical interest. We present a semi-automatic algorithm that uses a priori knowledge of prostate shape to arrive at the final prostate contour. The contour of one slice is then used as initial estimate in the neighboring slices. Thus we propagate the contour in 3D through steps of refinement in each slice. The algorithm makes only minimum assumptions about the prostate shape. A statistical shape model of prostate contour in polar transform space is employed to narrow search space. Further, shape guidance is implicitly imposed by allowing only plausible edge orientations using template matching. The algorithm does not require region-homogeneity, discriminative edge force, or any particular edge profile. Likewise, it makes no assumption on the imaging coils and pulse sequences used and it is robust to the patient's pose (supine, prone, etc.). The contour method was validated using expert segmentation on clinical MRI data. We recorded a mean absolute distance of 2.0 ± 0.6 mm and dice similarity coefficient of 0.93 ± 0.3 in midsection. The algorithm takes about 1 second per slice.
随着磁共振成像(MRI)可能成为前列腺癌检测和分期的首选方式,在感兴趣的临床区域内需要快速准确地勾勒出前列腺轮廓。我们提出了一种半自动算法,该算法利用前列腺形状的先验知识来确定最终的前列腺轮廓。然后将一个切片的轮廓用作相邻切片的初始估计。因此,我们通过在每个切片中进行细化步骤,在三维空间中传播轮廓。该算法对前列腺形状仅做了最小限度的假设。采用极坐标变换空间中前列腺轮廓的统计形状模型来缩小搜索空间。此外,通过使用模板匹配仅允许合理的边缘方向,隐式地施加形状引导。该算法不需要区域均匀性、判别边缘力或任何特定的边缘轮廓。同样,它对所使用的成像线圈和脉冲序列不做任何假设,并且对患者的姿势(仰卧、俯卧等)具有鲁棒性。使用临床MRI数据上的专家分割对轮廓方法进行了验证。我们记录了在中部区域平均绝对距离为2.0±0.6毫米,骰子相似系数为0.93±0.3。该算法每切片大约需要1秒。