An optimal brain tumor segmentation algorithm for clinical MRI dataset with low resolution and non-contiguous slices

BACKGROUND: Segmenting brain tumor and its constituent regions from magnetic resonance images (MRI) is important for planning diagnosis and treatment. In clinical routine often an experienced radiologist delineates the tumor regions using multimodal MRI. But this manual segmentation is prone to poor reproducibility and is time consuming. Also, routine clinical scans are usually of low resolution. To overcome these limitations an automated and precise segmentation algorithm based on computer vision is needed. METHODS: We investigated the performance of three widely used segmentation methods namely region growing, fuzzy C means and deep neural networks (deepmedic). We evaluated these algorithms on the BRATS 2018 dataset by choosing randomly 48 patients data (high grade, n = 24 and low grade, n = 24) and on our routine clinical MRI brain tumor dataset (high grade, n = 15 and low grade, n = 28). We measured their performance using dice similarity coefficient, Hausdorff distance and volume measures. RESULTS: Region growing method performed very poorly when compared to fuzzy C means (fcm) and deepmedic network. Dice similarity coefficient scores for FCM and deepmedic algorithms were close to each other for BRATS and clinical dataset. The accuracy was below 70% for both these methods in general. CONCLUSION: Even though the deepmedic network showed very high accuracy in BRATS challenge for brain tumor segmentation, it has to be custom trained for the low resolution routine clinical scans. It also requires large training data to be used as a stand-alone algorithm for clinical applications. Nevertheless deepmedic may be a better algorithm for brain tumor segmentation when compared to region growing or FCM.