Prostate segmentations are required for an ever-increasing number of medical applications, such as image-based lesion detection, fusion-guided biopsy and focal therapies. However, obtaining accurate segmentations is laborious, requires expertise and, even then, the inter-observer variability remains high. In this paper, a robust, accurate and generalizable model for Magnetic Resonance (MR) and three-dimensional (3D) Ultrasound (US) prostate image segmentation is proposed. It uses a densenet-resnet-based Convolutional Neural Network (CNN) combined with techniques such as deep supervision, checkpoint ensembling and Neural Resolution Enhancement. The MR prostate segmentation model was trained with five challenging and heterogeneous MR prostate datasets (and two US datasets), with segmentations from many different experts with varying segmentation criteria. The model achieves a consistently strong performance in all datasets independently (mean Dice Similarity Coefficient -DSC- above 0.91 for all datasets except for one), outperforming the inter-expert variability significantly in MR (mean DSC of 0.9099 vs. 0.8794). When evaluated on the publicly available Promise12 challenge dataset, it attains a similar performance to the best entries. In summary, the model has the potential of having a significant impact on current prostate procedures, undercutting, and even eliminating, the need of manual segmentations through improvements in terms of robustness, generalizability and output resolution.

中文翻译：

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通过卷积神经网络在磁共振和超声图像中进行稳健的分辨率增强前列腺分割

对于越来越多的医学应用，例如基于图像的病变检测，融合引导活检和局灶治疗，需要进行前列腺分割术。然而，获得准确的分割是费力的，需要专业知识，并且即使如此，观察者之间的可变性仍然很高。本文提出了一个健壮，准确，可推广的磁共振（MR）和三维（3D）超声（US）前列腺图像分割模型。它使用基于密集网-资源的卷积神经网络（CNN），并结合了诸如深度监督，检查点集合和神经分辨率增强等技术。通过五个具有挑战性的异构MR前列腺数据集（和两个US数据集）对MR前列腺分割模型进行了训练，来自具有不同细分标准的许多不同专家的细分。该模型可在所有数据集中独立获得稳定的稳定表现（平均骰子相似系数-DSC-除一个数据集外，所有数据集均高于0.91，在MR方面明显优于专家间差异（平均DSC为0.9099对0.8794）。在可公开获取的Promise12挑战数据集上进行评估时，其性能与最佳项目相似。总之，该模型具有对当前前列腺手术产生重大影响的潜力，通过改进鲁棒性，通用性和输出分辨率，可以减少甚至消除对手动分割的需求。