IEEE Trans Med Imaging. 2020 Dec;39(12):4174-4185. doi: 10.1109/TMI.2020.3014433. Epub 2020 Nov 30.
Fully convolutional neural networks have made promising progress in joint liver and liver tumor segmentation. Instead of following the debates over 2D versus 3D networks (for example, pursuing the balance between large-scale 2D pretraining and 3D context), in this paper, we novelly identify the wide variation in the ratio between intra- and inter-slice resolutions as a crucial obstacle to the performance. To tackle the mismatch between the intra- and inter-slice information, we propose a slice-aware 2.5D network that emphasizes extracting discriminative features utilizing not only in-plane semantics but also out-of-plane coherence for each separate slice. Specifically, we present a slice-wise multi-input multi-output architecture to instantiate such a design paradigm, which contains a Multi-Branch Decoder (MD) with a Slice-centric Attention Block (SAB) for learning slice-specific features and a Densely Connected Dice (DCD) loss to regularize the inter-slice predictions to be coherent and continuous. Based on the aforementioned innovations, we achieve state-of-the-art results on the MICCAI 2017 Liver Tumor Segmentation (LiTS) dataset. Besides, we also test our model on the ISBI 2019 Segmentation of THoracic Organs at Risk (SegTHOR) dataset, and the result proves the robustness and generalizability of the proposed method in other segmentation tasks.
全卷积神经网络在肝脏和肝脏肿瘤联合分割方面取得了令人瞩目的进展。本文没有陷入二维与三维网络的争论（例如，在二维大预训练与三维上下文之间寻求平衡），而是新颖地发现，切片内和切片间分辨率之比的广泛变化是性能的一个关键障碍。为了解决切片内和切片间信息之间的不匹配问题，我们提出了一种切片感知的 2.5D 网络，该网络强调利用每个单独切片的平面内语义和平面外一致性来提取有区别的特征。具体来说，我们提出了一种切片式多输入多输出架构来实例化这种设计范式，其中包含一个具有切片中心注意力模块的多分支解码器，用于学习切片特定的特征，以及一个密集连接的 Dice 损失，以正则化切片间预测，使其具有一致性和连续性。基于上述创新，我们在 MICCAI 2017 肝脏肿瘤分割（LiTS）数据集上取得了最先进的结果。此外，我们还在 ISBI 2019 胸部危险器官分割（SegTHOR）数据集上测试了我们的模型，结果证明了所提出的方法在其他分割任务中的稳健性和通用性。
