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Assessing the Relevance of Multi-planar MRI Acquisitions for Prostate Segmentation Using Deep Learning Techniques

机译：使用深度学习技术评估多平面MRI采集与前列腺分割的相关性

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Background. Prostate segmentation is a crucial step in computer-aided systems for prostate cancer detection. Multi-planar acquisitions are commonly used by clinicians to obtain a more accurate patient diagnosis but their relevance in prostate segmentation using fully automated algorithms has not been assessed. To date, the limited assessment of this relevance stems from the fact that both axial and sagittal prostate imaging views, as opposed to a single view, doubles the acquisition time. In this work, we assess the relevance of multi-planar imaging for prostate segmentation within a deep learning segmentation framework. Materials and Methods. We propose a deep learning prostate segmentation framework either from either axial or from axial and sagittal T2-weighted magnetic resonance images (MRI). The system is based on an ensemble of convolutional neural networks, each independently trained on a single imaging view. We compare single-view (axial) segmentations to those obtained from two imaging views (axial and sagittal) to assess the relevance of using multi-planar acquisitions. Algorithm performance assessment will be two-fold: 1) the global DICE score between the algorithm's predictions and the segmentations of an experienced reader will be computed and 2) the number of lesions located within the algorithm's segmentation prediction will be calculated. A subset of 80 patients from the public PROSTATEx-2 database containing both axial and sagittal T2-weighted MRIs will be used for this study. Results. The multi-planar network outperformed the network trained on only axial views according to both the proposed metrics. A statistically significant increase of 4% in DICE scores was found along with an 9% increase in the number of lesions within the predicted segmentation. Conclusions. The proposed method allows for a fully automatic segmentation of the prostate from single- or multi-view MRI and assesses the relevance of multi-planar MRI acquisitions for fully automatic prostate segmentation algorithms.
机译：背景。前列腺分割是前列腺癌检测计算机辅助系统中的关键步骤。临床医生通常使用多平面采集来获得更准确的患者诊断，但尚未评估使用全自动算法在前列腺分割中的相关性。迄今为止，对这种相关性的有限评估是基于这样的事实，即轴向和矢状前列腺成像视图与单个视图相反，会使获取时间加倍。在这项工作中，我们评估了深度学习分割框架内多平面成像与前列腺分割的相关性。材料和方法。我们从轴向或轴向和矢状T2加权磁共振图像（MRI）提出了深度学习前列腺分割框架。该系统基于卷积神经网络的集成，每个都在单个成像视图上进行独立训练。我们将单视图（轴向）分割与从两个成像视图（轴向和矢状）获得的分割进行比较，以评估使用多平面采集的相关性。算法性能评估将有两个方面：1）将计算算法预测与有经验的读者的细分之间的总体DICE得分，以及2）将计算算法的细分预测内的病变数量。这项研究将使用公共P​​ROSTATEx-2数据库中包含轴向和矢状T2加权MRI的80例患者的子集。结果。根据这两个建议的指标，多平面网络的性能优于仅在轴向视图上训练的网络。发现DICE分数的统计显着增加为4％，而在预测的细分范围内，病变数目的增加为9％。结论所提出的方法允许从单视图或多视图MRI进行前列腺的全自动分割，并评估多平面MRI采集与全自动前列腺分割算法的相关性。

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