One of the distinct characteristics in radiologists' reading of multiparametric prostate MR scans, using reporting systems such as PI-RADS v2.1, is to score individual types of MR modalities, T2-weighted, diffusion-weighted, and dynamic contrast-enhanced, and then combine these image-modality-specific scores using standardised decision rules to predict the likelihood of clinically significant cancer. This work aims to demonstrate that it is feasible for low-dimensional parametric models to model such decision rules in the proposed Combiner networks, without compromising the accuracy of predicting radiologic labels: First, it is shown that either a linear mixture model or a nonlinear stacking model is sufficient to model PI-RADS decision rules for localising prostate cancer. Second, parameters of these (generalised) linear models are proposed as hyperparameters, to weigh multiple networks that independently represent individual image modalities in the Combiner network training, as opposed to end-to-end modality ensemble. A HyperCombiner network is developed to train a single image segmentation network that can be conditioned on these hyperparameters during inference, for much improved efficiency. Experimental results based on data from 850 patients, for the application of automating radiologist labelling multi-parametric MR, compare the proposed combiner networks with other commonly-adopted end-to-end networks. Using the added advantages of obtaining and interpreting the modality combining rules, in terms of the linear weights or odds-ratios on individual image modalities, three clinical applications are presented for prostate cancer segmentation, including modality availability assessment, importance quantification and rule discovery.

翻译：放射科医师在使用PI-RADS v2.1等报告系统判读多参数前列腺MR扫描时，其显著特征之一是对T2加权像、弥散加权像和动态增强像等各模态MR图像进行独立评分，随后通过标准化决策规则融合这些图像模态特异性评分，以预测临床显著性癌症的发生概率。本研究旨在证明，低维参数模型能够在所提出的Combiner网络中有效建模此类决策规则，且不影响放射学标签预测的准确性：首先，研究表明线性混合模型或非线性堆叠模型均足以模拟用于前列腺癌定位的PI-RADS决策规则。其次，将上述（广义）线性模型的参数作为超参数，在Combiner网络训练中对独立表征各图像模态的多个网络进行加权（而非采用端到端模态集成）。基于此开发的HyperCombiner网络可在推理阶段受控于这些超参数，训练单一图像分割网络，从而显著提升效率。基于850例患者数据的实验（应用于放射科医师多参数MR标注自动化）将所提出的Combiner网络与其他常用端到端网络进行了比较。通过获取并解析模态融合规则（以各图像模态的线性权重或比值比形式呈现），本文展示了三项针对前列腺癌分割的临床应用：模态可用性评估、重要性量化及规则发现。