Abstract：
Over the past two decades, machine analysis of medical imaging has advanced rapidly, opening up significant potential for several important medical applications. As complicated diseases increase and the number of cases rises, the role of machine-based imaging analysis has become indispensable. It serves as both a tool and an assistant to medical experts, providing valuable insights and guidance. A particularly challenging task in this area is lesion segmentation, a task that is challenging even for experienced radiologists. The complexity of this task highlights the urgent need for robust machine learning approaches to support medical staff. In response, we present our novel solution: the D-TrAttUnet architecture. This framework is based on the observation that different diseases often target specific organs. Our architecture includes an encoder-decoder structure with a composite Transformer-CNN encoder and dual decoders. The encoder includes two paths: the Transformer path and the Encoders Fusion Module path. The Dual-Decoder configuration uses two identical decoders, each with attention gates. This allows the model to simultaneously segment lesions and organs and integrate their segmentation losses. To validate our approach, we performed evaluations on the Covid-19 and Bone Metastasis segmentation tasks. We also investigated the adaptability of the model by testing it without the second decoder in the segmentation of glands and nuclei. The results confirmed the superiority of our approach, especially in Covid-19 infections and the segmentation of bone metastases. In addition, the hybrid encoder showed exceptional performance in the segmentation of glands and nuclei, solidifying its role in modern medical image analysis.
摘要：
在过去的二十年里，医学影像的机器分析发展迅速，为几个重要的医疗应用开辟了巨大的潜力。随着复杂疾病的增加和病例数的增加，基于机器的成像分析的作用已经变得不可或缺。它既是医学专家的工具，也是医学专家的助手，提供有价值的见解和指导。在这个领域一个特别具有挑战性的任务是病变分割，即使对于经验丰富的放射科医生来说，这项任务也具有挑战性。这项任务的复杂性凸显了迫切需要强大的机器学习方法来支持医务人员。作为回应,我们提出了我们的新解决方案：D-TrAttUnet体系结构。该框架基于不同疾病通常靶向特定器官的观察。我们的架构包括具有复合Transformer-CNN编码器和双解码器的编码器-解码器结构。编码器包括两个路径:变换器路径和编码器融合模块路径。双解码器配置使用两个相同的解码器，每个人都有注意门。这允许模型同时分割病变和器官并整合它们的分割损失。为了验证我们的方法，我们对Covid-19和骨转移分割任务进行了评估。我们还通过在没有第二个解码器的情况下对腺体和细胞核的分割进行测试来研究模型的适应性。结果证实了我们方法的优越性，特别是在新冠肺炎感染和骨转移的分割中。此外,混合编码器在腺体和细胞核的分割中表现出卓越的性能，巩固其在现代医学图像分析中的作用。
