Image segmentation, real-value prediction, and cross-modal translation are
critical challenges in medical imaging. In this study, we propose a versatile
multi-task neural network framework, based on an enhanced Transformer U-Net
architecture, capable of simultaneously, selectively, and adaptively addressing
these medical image tasks. Validation is performed on a public repository of
human brain MR and CT images. We decompose the traditional problem of
synthesizing CT images into distinct subtasks, which include skull
segmentation, Hounsfield unit (HU) value prediction, and image sequential
reconstruction. To enhance the framework's versatility in handling multi-modal
data, we expand the model with multiple image channels. Comparisons between
synthesized CT images derived from T1-weighted and T2-Flair images were
conducted, evaluating the model's capability to integrate multi-modal
information from both morphological and pixel value perspectives.

本研究提出了一种基于增强的 Transformer U-Net 架构的多功能多任务神经网络框架，可以同时、选择性和自适应地解决医学图像中的图像分割、实值预测和跨模态转换等关键挑战。通过对人脑 MR 和 CT 图像的公共数据集进行验证，我们将合成 CT 图像的传统问题分解为不同的子任务，包括颅骨分割、HU 值预测和图像顺序重建。为了增强该框架处理多模态数据的多功能性，我们在模型中添加了多个图像通道，并进行了基于 T1 加权和 T2-Flair 图像合成的 CT 图像比较，评估了模型从形态学和像素值角度集成多模态信息的能力。