标题： MMIS-Net用于视网膜液体分割和检测 显示英文标题

标题： MMIS-Net for Retinal Fluid Segmentation and Detection

摘要： 目的：深度学习方法在医学图像的分割和疾病检测方面已显示出有希望的结果。然而，大多数方法都是在来自单一来源、模态、器官或疾病类型的数据显示训练和测试，忽略了其他可用注释数据的综合潜力。许多来自不同模态、器官和疾病的中小型注释医学图像数据集是公开可用的。在本工作中，我们旨在利用这些数据集的协同潜力，以提高对未见过的数据的性能。方法：为此，我们提出了一种称为MMIS-Net（多模态医学图像分割网络）的新算法，该算法具有相似性融合块，利用监督和像素级相似性知识选择进行特征图融合。此外，为了解决不一致的类别定义和标签矛盾，我们创建了一个独热标签空间，以处理在一个数据集中不存在但在另一个数据集中注释的类别。MMIS-Net在10个数据集上进行了训练，涵盖了2种模态下的19个器官，以构建一个模型。结果：该算法在RETOUCH大型挑战隐藏测试集上进行了评估，优于用于医学图像分割的大规模基础模型和其他最先进的算法。我们在流体分割任务中取得了0.83的最佳平均Dice分数和0.035的绝对体积差异，以及在流体检测任务中取得了1的完美曲线下面积。结论：定量结果突显了我们所提出的模型的有效性，这是由于将相似性融合块纳入网络主干进行监督和相似性知识选择，以及使用独热标签空间来解决标签类别不一致和矛盾。 显示英文摘要

摘要： Purpose: Deep learning methods have shown promising results in the segmentation, and detection of diseases in medical images. However, most methods are trained and tested on data from a single source, modality, organ, or disease type, overlooking the combined potential of other available annotated data. Numerous small annotated medical image datasets from various modalities, organs, and diseases are publicly available. In this work, we aim to leverage the synergistic potential of these datasets to improve performance on unseen data. Approach: To this end, we propose a novel algorithm called MMIS-Net (MultiModal Medical Image Segmentation Network), which features Similarity Fusion blocks that utilize supervision and pixel-wise similarity knowledge selection for feature map fusion. Additionally, to address inconsistent class definitions and label contradictions, we created a one-hot label space to handle classes absent in one dataset but annotated in another. MMIS-Net was trained on 10 datasets encompassing 19 organs across 2 modalities to build a single model. Results: The algorithm was evaluated on the RETOUCH grand challenge hidden test set, outperforming large foundation models for medical image segmentation and other state-of-the-art algorithms. We achieved the best mean Dice score of 0.83 and an absolute volume difference of 0.035 for the fluids segmentation task, as well as a perfect Area Under the Curve of 1 for the fluid detection task. Conclusion: The quantitative results highlight the effectiveness of our proposed model due to the incorporation of Similarity Fusion blocks into the network's backbone for supervision and similarity knowledge selection, and the use of a one-hot label space to address label class inconsistencies and contradictions.