论文标题
具有多尺度特征抽象的部分标记数据集对多器官分割
Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction
论文作者
论文摘要
完全注释的数据集缺乏是开发基于深度学习的图像分割算法的限制因素，并且在多器官分割中，问题变得更加明显。在本文中，我们提出了一种统一的培训策略，该策略使一个新型的多尺度深神经网络可以在多个部分标记的数据集中进行多器官细分进行培训。此外，提出了一种用于多尺度特征抽象的新网络体系结构，以将金字塔输入和特征分析整合到U形金字塔结构中。为了弥合直接合并不同尺度的特征引起的语义差距，引入了相等的卷积深度机制。此外，我们采用了深层监督机制，以不同的尺度来完善输出。为了充分利用所有量表的分割特征，我们设计一个自适应加权层以自动方式融合输出。将所有这些机制一起整合到金字塔输入金字塔输出特征抽象网络（PIPO-FAN）中。我们提出的方法在四个可公开可用的数据集上进行了评估，包括BTCV，LITS，套件和Spleen，并在其中实现了非常有前途的性能。这项工作的源代码在https://github.com/dial-rpi/pipo-fan上公开共享，以便其他人轻松地复制工作并使用引入的机制来构建自己的模型。
Shortage of fully annotated datasets has been a limiting factor in developing deep learning based image segmentation algorithms and the problem becomes more pronounced in multi-organ segmentation. In this paper, we propose a unified training strategy that enables a novel multi-scale deep neural network to be trained on multiple partially labeled datasets for multi-organ segmentation. In addition, a new network architecture for multi-scale feature abstraction is proposed to integrate pyramid input and feature analysis into a U-shape pyramid structure. To bridge the semantic gap caused by directly merging features from different scales, an equal convolutional depth mechanism is introduced. Furthermore, we employ a deep supervision mechanism to refine the outputs in different scales. To fully leverage the segmentation features from all the scales, we design an adaptive weighting layer to fuse the outputs in an automatic fashion. All these mechanisms together are integrated into a Pyramid Input Pyramid Output Feature Abstraction Network (PIPO-FAN). Our proposed method was evaluated on four publicly available datasets, including BTCV, LiTS, KiTS and Spleen, where very promising performance has been achieved. The source code of this work is publicly shared at https://github.com/DIAL-RPI/PIPO-FAN for others to easily reproduce the work and build their own models with the introduced mechanisms.
