| dc.description.abstract | Acoustic neuroma, meningiomas, and metastatic brain tumors are prevalent brain tumors. Gamma knife radiation therapy is a common treatment for neuromas and meningiomas, but postoperative edema is a usual complication. Metastatic brain tumors often lead to brain edema, making the observation of edema crucial. Currently, tumor and edema assessment rely on visual inspection by physicians, which is time-consuming and subject to subjective errors.
This research focused on deep learning for the segmentation and quantification of tumors and edema associated with acoustic neuroma as well as the segmentation and quantification of edema associated with meningiomas and metastatic brain tumors. The tumor segmentation of acoustic neuroma was conducted on T1-weighted post-contrast- enhancement (T1C) images using DeepMedic. The edema segmentation of acoustic neuroma, metastatic brain tumors, and meningiomas was conducted on T2-weighted images by first extracting the brain mask using Mask R-CNN and then applying DeepMedic for edema segmentation and quantification.
For the auditory nerve tumor segment, our dataset comprised 44 patients with 44 data points. After undergoing five-fold cross-validation, our model achieved an average Dice coefficient of 91.9%. Regarding the brain mask extraction, our dataset consisted of 60 patients with 60 data points. After five-fold cross-validation, our model achieved an average Dice coefficient of 94.3%. Concerning the edema segment, the auditory nerve tumor edema dataset included 10 patients with 44 data points. After five-fold cross-validation, the average Dice coefficient for auditory nerve tumor edema was 55.2%. For metastatic brain tumor edema, with a dataset of 33 patients and 66 data points, the average Dice coefficient after five-fold cross-validation was 83.6%. Lastly, for meningioma edema, with a dataset of 20 patients and 130 data points, the average Dice coefficient after five-fold cross-validation was 76.8%. We have also developed a graphical user interface to facilitate easy clinical use by physicians.
This study enables the automated segmentation and quantification of auditory nerve tumor and its edema, as well as edema associated with meningioma and metastatic brain tumors. It aids physicians in assessing the progression, quantifying severity, and studying case variations in edema. The research provides more accurate and objective data for physicians to reference when determining treatment directions, ultimately enhancing diagnostic efficiency and accuracy. Additionally, a graphical user interface has been developed to facilitate convenient operation for physicians. | en_US |