Medical image segmentation is the process of anatomically isolating organs for analysis and treatment. Leading works within this domain emerged with the well-known U-Net. Despite its success, recent works have shown the limitations of U-Net to conduct segmentation given image particularities such as noise, corruption or lack of contrast. Prior knowledge integration allows to overcome segmentation ambiguities. This paper introduces BB-UNet (Bounding Box U-Net), a deep learning model that integrates location as well as shape prior onto model training. The proposed model is inspired by U-Net and incorporates priors through a novel convolutional layer introduced at the level of skip connections. The proposed architecture helps in presenting attention kernels onto the neural training in order to guide the model on where to look for the organs. Moreover, it fine-tunes the encoder layers based on positional constraints. The proposed model is exploited within two main paradigms: as a solo model given a fully supervised framework and as an ancillary model, in a weakly supervised setting. In the current experiments, manual bounding boxes are fed at inference and as such BB-Unet is exploited in a semi-automatic setting; however, BB-Unet has the potential of being part of a fully automated process, if it relies on a preliminary step of object detection. To validate the performance of the proposed model, experiments are conducted on two public datasets: the SegTHOR dataset which focuses on the segmentation of thoracic organs at risk in computed tomography (CT) images, and the Cardiac dataset which is a mono-modal MRI dataset released as part of the Decathlon challenge and dedicated to segmentation of the left atrium. Results show that the proposed method outperforms state-of-the-art methods in fully supervised learning frameworks and registers relevant results given the weakly supervised domain.

中文翻译：

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BB-UNet：带有边界框先验的 U-Net

医学图像分割是在解剖学上分离器官以进行分析和治疗的过程。该领域的领先作品出现在著名的 U-Net 中。尽管取得了成功，但最近的工作表明 U-Net 在进行图像分割方面存在局限性，例如噪声、损坏或缺乏对比度。先验知识集成允许克服分割歧义。本文介绍了 BB-UNet（Bounding Box U-Net），这是一种将位置和形状先验集成到模型训练中的深度学习模型。所提出的模型受到 U-Net 的启发，并通过在跳过连接级别引入的新颖卷积层结合了先验。所提出的架构有助于将注意力内核呈现到神经训练中，以指导模型在哪里寻找器官。而且，它根据位置约束微调编码器层。所提出的模型在两个主要范式中被利用：作为给定完全监督框架的单独模型和作为弱监督环境中的辅助模型。在当前的实验中，手动边界框是在推理时输入的，因此 BB-Unet 在半自动设置中被利用；然而，如果 BB-Unet 依赖于对象检测的初步步骤，它有可能成为全自动过程的一部分。为了验证所提出模型的性能，在两个公共数据集上进行了实验：SegTHOR 数据集专注于计算机断层扫描 (CT) 图像中存在风险的胸部器官的分割，Cardiac 数据集是作为 Decathlon 挑战的一部分发布的单模态 MRI 数据集，专门用于左心房的分割。结果表明，所提出的方法在完全监督的学习框架中优于最先进的方法，并在弱监督域的情况下注册相关结果。