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首页> 外文期刊>Journal of digital imaging: the official journal of the Society for Computer Applications in Radiology >Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning
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Performance of a Deep Neural Network Algorithm Based on a Small Medical Image Dataset: Incremental Impact of 3D-to-2D Reformation Combined with Novel Data Augmentation, Photometric Conversion, or Transfer Learning

机译：基于小型医学图像数据集的深神经网络算法的性能：3D-2D改革的增量影响结合新型数据增强，光度转换或转移学习

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摘要

Collecting and curating large medical-image datasets for deep neural network (DNN) algorithm development is typically difficult and resource-intensive. While transfer learning (TL) decreases reliance on large data collections, current TL implementations are tailored to two-dimensional (2D) datasets, limiting applicability to volumetric imaging (e.g., computed tomography). Targeting performance enhancement of a DNN algorithm based on a small image dataset, we assessed incremental impact of 3D-to-2D projection methods, one supporting novel data augmentation (DA); photometric grayscale-to-color conversion (GCC); and/or TL on training of an algorithm from a small coronary computed tomography angiography (CCTA) dataset (200 examinations, 50% with atherosclerosis and 50% atherosclerosis-free) producing 245 diseased and 1127 normal coronary arteries/branches. Volumetric CCTA data was converted to a 2D format creating both an Aggregate Projection View (APV) and a Mosaic Projection View (MPV), supporting DA per vessel; both grayscale and color-mapped versions of each view were also obtained. Training was performed both without and with TL, and algorithm performance of all permutations was compared using area under the receiver operating characteristics curve. Without TL, APV performance was 0.74 and 0.87 on grayscale and color images, respectively, compared to 0.90 and 0.87 for MPV. With TL, APV performance was 0.78 and 0.88 on grayscale and color images, respectively, compared with 0.93 and 0.91 for MPV. In conclusion, TL enhances performance of a DNN algorithm from a small volumetric dataset after proposed 3D-to-2D reformatting, but additive gain is achieved with application of either GCC to APV or the proposed novel MPV technique for DA.
机译：用于深度神经网络（DNN）算法开发的集收集和策划大型医学图像数据集通常困难和资源密集。虽然转移学习（TL）降低了对大数据收集的依赖，但是当前的TL实现定制到二维（2D）数据集，限制了对体积成像（例如，计算机断层扫描）的适用性。针对基于小图像数据集的DNN算法的性能增强，我们评估了3D-2D投影方法的增量影响，一个支持新型数据增强（DA）;光度灰度 - 颜色转换（GCC）;和/或TL关于从小冠状动脉造影血管造影（CCTA）数据集（CCTA）数据集的算法（200例检查，动脉粥样硬化和50％的动脉粥样硬化）产生245例患病和1127个正常冠状动脉/分支。体积CCTA数据被转换为2D格式，创建聚合投影视图（APV）和马赛克投影视图（MPV），支撑DA每个容器;还获得了每个视图的灰度和颜色映射版本。训练在没有和TL的情况下进行，并且使用接收器操作特性曲线下的区域进行比较所有排列的算法性能。在灰度和彩色图像中，APV性能分别为0.74和0.87，而MPV为0.90和0.87。对于TL，APV性能分别为0.78和0.88，分别在灰度和彩色图像上，与MPV为0.93和0.91。总之，TL在提出的3D到2D重新格式化之后从小容量数据集中增强了DNN算法的性能，但是通过将GCC应用于APV或用于DA的所提出的新型MPV技术来实现添加剂增益。

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