Multiatlas-Based Segmentation Editing With Interaction-Guided Patch Selection and Label Fusion

Abstract

We propose a novel multi-atlas based segmentation method to address the segmentation editing scenario, where an incomplete segmentation is given along with a set of existing reference label images (used as atlases). Unlike previous multi-atlas based methods, which depend solely on appearance features, we incorporate interaction-guided constraints to find appropriate atlas label patches in the reference label set and derive their weights for label fusion. Specifically, user interactions provided on the erroneous parts are first divided into multiple local combinations. For each combination, the atlas label patches well-matched with both interactions and the previous segmentation are identified. Then, the segmentation is updated through the voxel-wise label fusion of selected atlas label patches with their weights derived from the distances of each underlying voxel to the interactions. Since the atlas label patches well-matched with different local combinations are used in the fusion step, our method can consider various local shape variations during the segmentation update, even with only limited atlas label images and user interactions. Besides, since our method does not depend on either image appearance or sophisticated learning steps, it can be easily applied to general editing problems. To demonstrate the generality of our method, we apply it to editing segmentations of CT prostate, CT brainstem, and MR hippocampus, respectively. Experimental results show that our method outperforms existing editing methods in all three data sets.

Computational Complexity Reduction of Neural Networks of Brain Tumor Image Segmentation by Introducing Fermi–Dirac Correction Functions

“…The relevant fruitful achievements also facilitate and change the fashion of synergy between clinical diagnosis and computerized assistance. The scope of application covers the demands of computer-guided pathological inspection [ 1 , 2 , 3 , 4 ], brain neural circuit mapping and tracking [ 1 , 5 , 6 , 7 , 8 , 9 ], specific tissue detection in image-based datasets [ 5 , 10 , 11 , 12 , 13 ], and other clinical applications. Among these applications, neural network (NN)-based recognition methods that are capable of detecting life-threatening abnormalities from image-based datasets especially attract the attention of both scientific and engineering participants [ 2 , 11 , 14 , 15 , 16 , 17 ] and gradually replace conventional approaches.…”