School of Information Science and Technology, East China University of Science and Technology, Shanghai, P. R. China.
Shanghai Engineering Research Center of Internet of Things for Respiratory Medicine, Shanghai, P. R. China.
Med Phys. 2022 Nov;49(11):7001-7015. doi: 10.1002/mp.15861. Epub 2022 Jul 30.
The accurate and reliable segmentation of prostate cancer (PCa) lesions using multiparametric magnetic resonance imaging (mpMRI) sequences, is crucial to the image-guided intervention and treatment of prostate disease. For PCa lesion segmentation, it is essential to reliably combine local and global information to retain the features of small targets at multiple scales. Therefore, this study proposes a multi-scale segmentation network with a cascading pyramid convolution module (CPCM) and a double-input channel attention module (DCAM) for the automated and accurate segmentation of PCa lesions using mpMRI.
First, the region of interest was extracted from the data by clipping to enlarge the target region and reduce the background noise interference. Next, four CPCMs with large convolution kernels in their skip connection paths were designed to improve the feature extraction capability of the network for small targets. At the same time, a convolution decomposition was applied to reduce the computational complexity. Finally, the DCAM was adopted in the decoder to provide bottom-up semantic discriminative guidance; it can use the semantic information of the network's deep features to guide the shallow output of features with a higher discriminant ability. A residual refinement module (RRM) was also designed to strengthen the recognition ability of each stage. The feature maps of the skip connection and the decoder all go through the RRM.
For the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) dataset, our proposed model achieved a Dice similarity coefficient (DSC) of 79.31% and an average boundary distance (ABD) of 4.15 mm. For the Prostate Multiparametric MRI (PROMM) dataset, our method greatly improved the DSC to 82.11% and obtained an ABD of 3.64 mm.
The experimental results of two different mpMRI prostate datasets demonstrate that our model is more accurate and reliable on small targets. In addition, it outperforms other state-of-the-art methods.
使用多参数磁共振成像（mpMRI）序列准确可靠地对前列腺癌（PCa）病变进行分割，对于前列腺疾病的图像引导干预和治疗至关重要。对于 PCa 病变分割，可靠地结合局部和全局信息以保留多尺度下小目标的特征至关重要。因此，本研究提出了一种具有级联金字塔卷积模块（CPCM）和双输入通道注意力模块（DCAM）的多尺度分割网络，用于使用 mpMRI 自动准确地分割 PCa 病变。
首先，通过裁剪提取感兴趣区域，以扩大目标区域并减少背景噪声干扰。接下来，设计了四个带有较大卷积核的 CPCM 作为 skip connection 路径，以提高网络对小目标的特征提取能力。同时，采用卷积分解来降低计算复杂度。最后，在解码器中采用 DCAM 提供自下而上的语义区分指导；它可以利用网络深层特征的语义信息来指导具有更高判别能力的浅层特征输出。还设计了一个残差细化模块（RRM）来增强每个阶段的识别能力。skip connection 和解码器的特征图都通过 RRM。
对于协作计算机视觉基准测试倡议（I2CVB）数据集，我们提出的模型获得了 79.31%的骰子相似系数（DSC）和 4.15mm 的平均边界距离（ABD）。对于前列腺多参数 MRI（PROMM）数据集，我们的方法大大提高了 DSC 至 82.11%，并获得了 3.64mm 的 ABD。
两个不同的 mpMRI 前列腺数据集的实验结果表明，我们的模型在小目标上更准确可靠。此外，它优于其他最先进的方法。
