Zhou Tongxue, Noeuveglise Alexandra, Modzelewski Romain, Ghazouani Fethi, Thureau Sébastien, Fontanilles Maxime, Ruan Su
School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China.
Department of Nuclear Medicine, Henri Becquerel Cancer Center, Rouen, 76038, France.
Comput Med Imaging Graph. 2023 Jun;106:102218. doi: 10.1016/j.compmedimag.2023.102218. Epub 2023 Mar 16.
Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predict brain tumor recurrence location plays an important role in the treatment planning and it can potentially prolong patient's survival time. There is still little work to deal with this issue. In this paper, we present a deep learning-based brain tumor recurrence location prediction network. Since the dataset is usually small, we propose to use transfer learning to improve the prediction. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Following that, a multi-scale multi-channel feature fusion model and a nonlinear correlation learning module are developed to learn the effective features. The correlation between multi-channel features is modeled by a nonlinear equation. To measure the similarity between the distributions of original features of one modality and the estimated correlated features of another modality, we propose to use Kullback-Leibler divergence. Based on this divergence, a correlation loss function is designed to maximize the similarity between the two feature distributions. Finally, two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location. To the best of our knowledge, this is the first work that can segment the present tumor and at the same time predict future tumor recurrence location, making the treatment planning more efficient and precise. The experimental results demonstrated the effectiveness of our proposed method to predict the brain tumor recurrence location from the limited dataset.
脑肿瘤是癌症死亡的主要原因之一。即使经过标准治疗，高级别脑肿瘤仍更容易复发。因此，开发一种预测脑肿瘤复发位置的方法在治疗规划中起着重要作用，并且有可能延长患者的生存时间。目前针对这个问题的研究仍然很少。在本文中，我们提出了一种基于深度学习的脑肿瘤复发位置预测网络。由于数据集通常较小，我们建议使用迁移学习来提高预测效果。我们首先在公开数据集BraTS 2021上训练一个多模态脑肿瘤分割网络。然后，将预训练的编码器迁移到我们的私有数据集上，以提取丰富的语义特征。接下来，开发了一个多尺度多通道特征融合模型和一个非线性相关性学习模块来学习有效特征。多通道特征之间的相关性通过一个非线性方程进行建模。为了衡量一种模态的原始特征分布与另一种模态的估计相关特征之间的相似性，我们建议使用库尔贝克-莱布勒散度。基于这种散度，设计了一个相关性损失函数，以最大化两个特征分布之间的相似性。最后，构建两个解码器来联合分割当前的脑肿瘤并预测其未来的肿瘤复发位置。据我们所知，这是第一项能够分割当前肿瘤并同时预测未来肿瘤复发位置的工作，从而使治疗规划更加高效和精确。实验结果证明了我们提出的方法在从有限数据集中预测脑肿瘤复发位置方面的有效性。
