Key Laboratory of Image Processing and Intelligent Control of Ministry of Education of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China. Xiangming Zhao and Minxin Huang contributed equally to this work.
Phys Med Biol. 2020 Aug 19;65(16):165013. doi: 10.1088/1361-6560/ab9453.
Fully convolutional neural network (FCN) has achieved great success in semantic segmentation. However, the performance of the FCN is generally compromised for multi-object segmentation. Multi-organ segmentation is very common while challenging in the field of medical image analysis, where organs largely vary with scales. Different organs are often treated equally in most segmentation networks, which is not quite optimal. In this work, we propose to divide a multi-organ segmentation task into multiple binary segmentation tasks by constructing a multi-to-binary network (MTBNet). The proposed MTBNet is based on the FCN for pixel-wise prediction. Moreover, we build a plug-and-play multi-to-binary block (MTB block) to adjust the influence of the feature maps from the backbone. This is achieved by parallelizing multiple branches with different convolutional layers and a probability gate (ProbGate). The ProbGate is set up for predicting whether the class exists, which is supervised clearly via an auxiliary loss without using any other inputs. More reasonable features are achieved by summing branches' features multiplied by the probability from the accompanying ProbGate and fed into a decoder module for prediction. The proposed method is validated on a challenging task dataset of multi-organ segmentation in abdominal MRI. Compared to classic medical and other multi-scale segmentation methods, the proposed MTBNet improves the segmentation accuracy of small organs by adjusting features from different organs and reducing the chance of missing or misidentifying these organs.
全卷积神经网络（FCN）在语义分割方面取得了巨大成功。然而，FCN 在多目标分割方面的性能通常会受到影响。在医学图像分析领域，多器官分割非常常见，但也极具挑战性，因为器官的大小差异很大。在大多数分割网络中，不同的器官通常被平等对待，这并不是最优的。在这项工作中，我们通过构建一个多到二进制网络（MTBNet），将多器官分割任务划分为多个二进制分割任务。所提出的 MTBNet 基于 FCN 进行像素级预测。此外，我们构建了一个即插即用的多到二进制块（MTB 块），通过使用概率门（ProbGate）和不同的卷积层并行化多个分支来调整来自骨干网的特征图的影响。ProbGate 用于预测类是否存在，这通过辅助损失进行明确监督，而无需使用任何其他输入。通过将来自伴随 ProbGate 的概率乘以分支的特征并将其输入解码器模块进行预测，从而获得更合理的特征。所提出的方法在腹部 MRI 多器官分割的挑战性任务数据集上进行了验证。与经典医学和其他多尺度分割方法相比，所提出的 MTBNet 通过调整来自不同器官的特征并减少这些器官的漏检或误检的机会，提高了小器官的分割精度。
