Title: Segmenting Medical MRI via Recurrent Decoding Cell Show Chinese title

Title: 通过循环解码单元进行医学MRI分割

Abstract: The encoder-decoder networks are commonly used in medical image segmentation due to their remarkable performance in hierarchical feature fusion. However, the expanding path for feature decoding and spatial recovery does not consider the long-term dependency when fusing feature maps from different layers, and the universal encoder-decoder network does not make full use of the multi-modality information to improve the network robustness especially for segmenting medical MRI. In this paper, we propose a novel feature fusion unit called Recurrent Decoding Cell (RDC) which leverages convolutional RNNs to memorize the long-term context information from the previous layers in the decoding phase. An encoder-decoder network, named Convolutional Recurrent Decoding Network (CRDN), is also proposed based on RDC for segmenting multi-modality medical MRI. CRDN adopts CNN backbone to encode image features and decode them hierarchically through a chain of RDCs to obtain the final high-resolution score map. The evaluation experiments on BrainWeb, MRBrainS and HVSMR datasets demonstrate that the introduction of RDC effectively improves the segmentation accuracy as well as reduces the model size, and the proposed CRDN owns its robustness to image noise and intensity non-uniformity in medical MRI. Show Chinese abstract

Abstract: 编码器-解码器网络由于在分层特征融合方面的出色表现，常用于医学图像分割。 然而，特征解码和空间恢复的扩展路径在融合不同层的特征图时没有考虑长期依赖关系，通用的编码器-解码器网络也没有充分利用多模态信息来提高网络鲁棒性，尤其是在分割医学MRI时。 在本文中，我们提出了一种称为循环解码单元（RDC）的新特征融合单元，该单元利用卷积RNN在解码阶段记忆前几层的长期上下文信息。 还基于RDC提出了一种名为卷积循环解码网络（CRDN）的编码器-解码网络，用于分割多模态医学MRI。 CRDN采用CNN主干来编码图像特征，并通过一系列RDC逐层解码以获得最终的高分辨率得分图。 在BrainWeb、MRBrainS和HVSMR数据集上的评估实验表明，引入RDC有效提高了分割精度并减少了模型大小，所提出的CRDN具有对医学MRI中图像噪声和强度不均匀性的鲁棒性。