Tumor Segmentation and Tumor Probability Analysis with Convolutional Neural Networks: Novel Strategies for Individualized Radiation Therapy of Head&Neck Cancer

• Applicants: Professor Dr. Michael Bock; Professor Dr. Nils Henrik Nicolay
• Subject Area: Nuclear Medicine, Radiotherapy, Radiobiology ; Medical Physics, Biomedical Technology
• Term: since 2021
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 443978314

The manual segmentation of tumor lesions is a tedious and user-dependent task in radiation therapy planning. Automatic segmentation algorithms have been proposed as an alternative, among which convolutional neural networks (CNN) exhibit the best segmentation performance if a sufficiently large amount of independent input imagedata is available. In radiation therapy planning of head&neck tumors, multi-contrast and multimodality imaging with MRI, PET and CT is often performed with up to 9 different contrasts per exam. These images are used to delineate the tumor margins and to identify hypoxic sub-regions in the tumor which require higher radiation dosesfor an efficient treatment. In this project CNNs will be trained on existing image data of a prospective clinical trial for head&neck tumor treatment. As neither the optimal architecture nor the best setting for the internal parameters is known for CNNs, multiple CNNs will be trained and compared using existing radiation plans as ground truth.In addition, pre-processing methods will be developed to correct for systematic image heterogeneity (originating for example from MRI systems with different field strength) so that more data can be included in the study. For this, the known MRI signal equations will be used to modify the signal intensities for each tissue type individually. It is time-consuming to acquire multimodality and multicontrast images.To identify those input image data that do not improve the segmentation performance, CNNs with different input data will be trained using a leave-one-out strategy, and the CNNs will be compared with a full CNN implementation. As a result of this optimization, the imaging protocol will be optimized and a CNN with optimal performance will be realized. With the resulting CNN configuration automatic tumor and lymph node segmentation will be performed, and correlated with a panel of established biological markers from histology to assess whether CNN-derived imaging features can predict individual tumor biology. The result of this project will be an optimized CNN for automaticsegmentation of head&neck tumors which will also provide additional radiomics information about ways to shorten the diagnostic imaging protocols and about hypoxia-related tumor heterogeneity, which might have direct consequences for new strategies to minimize tumor recurrence.

• DFG Programme: Research Grants