Zhou Sihang, Nie Dong, Adeli Ehsan, Yin Jianping, Lian Jun, Shen Dinggang
IEEE Trans Image Process. 2019 Jun 19. doi: 10.1109/TIP.2019.2919937.
Automatic image segmentation is an essential step for many medical image analysis applications, include computer-aided radiation therapy, disease diagnosis, and treatment effect evaluation. One of the major challenges for this task is the blurry nature of medical images (e.g., CT, MR and, microscopic images), which can often result in low-contrast and vanishing boundaries. With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder-decoder deep architectures. However, in many applications (with adjacent targets in blurry images), these models often fail to accurately locate complex boundaries and properly segment tiny isolated parts. In this paper, we aim to provide a method for blurry medical image segmentation and argue that skip connections are not enough to help accurately locate indistinct boundaries. Accordingly, we propose a novel high-resolution multi-scale encoder-decoder network (HMEDN), in which multi-scale dense connections are introduced for the encoder-decoder structure to finely exploit comprehensive semantic information. Besides skip connections, extra deeply-supervised high-resolution pathways (comprised of densely connected dilated convolutions) are integrated to collect high-resolution semantic information for accurate boundary localization. These pathways are paired with a difficulty-guided cross-entropy loss function and a contour regression task to enhance the quality of boundary detection. Extensive experiments on a pelvic CT image dataset, a multi-modal brain tumor dataset, and a cell segmentation dataset show the effectiveness of our method for 2D/3D semantic segmentation and 2D instance segmentation, respectively. Our experimental results also show that besides increasing the network complexity, raising the resolution of semantic feature maps can largely affect the overall model performance. For different tasks, finding a balance between these two factors can further improve the performance of the corresponding network.
自动图像分割是许多医学图像分析应用中的关键步骤，包括计算机辅助放射治疗、疾病诊断和治疗效果评估。这项任务的主要挑战之一是医学图像（如CT、MR和显微图像）的模糊特性，这通常会导致对比度低和边界消失。随着卷积神经网络的最新进展，图像分割取得了巨大进步，主要基于跳过连接的编码器-解码器深度架构。然而，在许多应用中（模糊图像中有相邻目标），这些模型往往无法准确地定位复杂边界，也无法正确分割微小的孤立部分。在本文中，我们旨在提供一种用于模糊医学图像分割的方法，并认为仅靠跳过连接不足以帮助准确定位不清晰的边界。因此，我们提出了一种新颖的高分辨率多尺度编码器-解码器网络（HMEDN），其中为编码器-解码器结构引入了多尺度密集连接，以精细地利用全面的语义信息。除了跳过连接外，还集成了额外的深度监督高分辨率路径（由密集连接的扩张卷积组成），以收集高分辨率语义信息用于准确的边界定位。这些路径与难度引导的交叉熵损失函数和轮廓回归任务相结合，以提高边界检测的质量。在盆腔CT图像数据集、多模态脑肿瘤数据集和细胞分割数据集上进行的大量实验分别表明了我们的方法在二维/三维语义分割和二维实例分割方面的有效性。我们的实验结果还表明，除了增加网络复杂度外，提高语义特征图的分辨率在很大程度上会影响整体模型性能。对于不同的任务，在这两个因素之间找到平衡可以进一步提高相应网络的性能。
