LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China.
LIST, Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, Nanjing, China; Centre de Recherche en Information Biomédicale Sino-Français (CRIBs), France.
Med Image Anal. 2021 Jul;71:102055. doi: 10.1016/j.media.2021.102055. Epub 2021 Apr 5.
Three-dimensional (3D) integrated renal structures (IRS) segmentation targets segmenting the kidneys, renal tumors, arteries, and veins in one inference. Clinicians will benefit from the 3D IRS visual model for accurate preoperative planning and intraoperative guidance of laparoscopic partial nephrectomy (LPN). However, no success has been reported in 3D IRS segmentation due to the inherent challenges in grayscale distribution: low contrast caused by the narrow task-dependent distribution range of regions of interest (ROIs), and the networks representation preferences caused by the distribution variation inter-images. In this paper, we propose the Meta Greyscale Adaptive Network (MGANet), the first deep learning framework to simultaneously segment the kidney, renal tumors, arteries and veins on CTA images in one inference. It makes innovations in two collaborate aspects: 1) The Grayscale Interest Search (GIS) adaptively focuses segmentation networks on task-dependent grayscale distributions via scaling the window width and center with two cross-correlated coefficients for the first time, thus learning the fine-grained representation for fine segmentation. 2) The Meta Grayscale Adaptive (MGA) learning makes an image-level meta-learning strategy. It represents diverse robust features from multiple distributions, perceives the distribution characteristic, and generates the model parameters to fuse features dynamically according to image's distribution, thus adapting the grayscale distribution variation. This study enrolls 123 patients and the average Dice coefficients of the renal structures are up to 87.9%. Fine selection of the task-dependent grayscale distribution ranges and personalized fusion of multiple representations on different distributions will lead to better 3D IRS segmentation quality. Extensive experiments with promising results on renal structures reveal powerful segmentation accuracy and great clinical significance in renal cancer treatment.
三维（3D）集成肾结构（IRS）分割的目标是在一次推断中分割肾脏、肾肿瘤、动脉和静脉。临床医生将受益于 3D IRS 可视化模型，以进行准确的腹腔镜部分肾切除术（LPN）术前规划和术中指导。然而，由于灰度分布固有的挑战，3D IRS 分割尚未取得成功：由于感兴趣区域（ROI）的任务相关分布范围较窄导致对比度低，以及由于图像之间的分布变化导致网络表示偏好。在本文中，我们提出了元灰度自适应网络（MGANet），这是第一个在一次推断中同时分割 CTA 图像上的肾脏、肾肿瘤、动脉和静脉的深度学习框架。它在两个协作方面进行了创新：1）灰度兴趣搜索（GIS）通过使用两个互相关系数来首次自适应地缩放窗口宽度和中心，从而将分割网络聚焦在任务相关的灰度分布上，从而学习精细分割的细粒度表示。2）元灰度自适应（MGA）学习是一种图像级元学习策略。它代表了来自多个分布的多种稳健特征，感知分布特征，并根据图像的分布动态生成模型参数来融合特征，从而适应灰度分布变化。本研究共纳入 123 例患者，肾脏结构的平均 Dice 系数高达 87.9%。精细选择任务相关的灰度分布范围和个性化融合不同分布的多种表示形式，将导致更好的 3D IRS 分割质量。对肾脏结构进行了广泛的实验，结果表明该方法具有强大的分割准确性，在肾癌治疗中具有重要的临床意义。
