TY - GEN
T1 - Hierarchical label fusion with multiscale feature representation and label-specific patch partition
AU - Wu, Guorong
AU - Shen, Dinggang
PY - 2014
Y1 - 2014
N2 - Recently, patch-based label fusion methods have achieved many successes in medical imaging area. After registering atlas images to the target image, the label at each target image point can be subsequently determined by checking the patchwise similarities between the underlying target image patch and all atlas image patches. Apparently, the definition of patchwise similarity is critical in label fusion. However, current methods often simply use entire image patch with fixed patch size throughout the entire label fusion procedure, which could be insufficient to distinguish complex shape/appearance patterns of anatomical structures in medical imaging scenario. In this paper, we address the above limitations at three folds. First, we assign each image patch with multiscale feature representations such that both local and semi-local image information can be encoded to increase robustness of measuring patchwise similarity in label fusion. Second, since multiple variable neighboring structures could present in one image patch, simply computing patchwise similarity based on the entire image patch is not specific to the particular structure of interest under labeling and can be easily misled by the surrounding variable structures in the same image patch. Thus, we partition each atlas patch into a set of new label-specific atlas patches according to the existing label information in the atlas images. Then, the new label-specific atlas patches can be more specific and flexible for label fusion than using the entire image patch, since the complex image patch has now been semantically divided into several distinct patterns. Finally, in order to correct the possible mis-labeling, we hierarchically improve the label fusion result in a coarse-to-fine manner by iteratively repeating the label fusion procedure with the gradually-reduced patch size. More accurate label fusion results have been achieved by our hierarchical label fusion method with multiscale feature presentations upon label-specific atlas patches.
AB - Recently, patch-based label fusion methods have achieved many successes in medical imaging area. After registering atlas images to the target image, the label at each target image point can be subsequently determined by checking the patchwise similarities between the underlying target image patch and all atlas image patches. Apparently, the definition of patchwise similarity is critical in label fusion. However, current methods often simply use entire image patch with fixed patch size throughout the entire label fusion procedure, which could be insufficient to distinguish complex shape/appearance patterns of anatomical structures in medical imaging scenario. In this paper, we address the above limitations at three folds. First, we assign each image patch with multiscale feature representations such that both local and semi-local image information can be encoded to increase robustness of measuring patchwise similarity in label fusion. Second, since multiple variable neighboring structures could present in one image patch, simply computing patchwise similarity based on the entire image patch is not specific to the particular structure of interest under labeling and can be easily misled by the surrounding variable structures in the same image patch. Thus, we partition each atlas patch into a set of new label-specific atlas patches according to the existing label information in the atlas images. Then, the new label-specific atlas patches can be more specific and flexible for label fusion than using the entire image patch, since the complex image patch has now been semantically divided into several distinct patterns. Finally, in order to correct the possible mis-labeling, we hierarchically improve the label fusion result in a coarse-to-fine manner by iteratively repeating the label fusion procedure with the gradually-reduced patch size. More accurate label fusion results have been achieved by our hierarchical label fusion method with multiscale feature presentations upon label-specific atlas patches.
UR - http://www.scopus.com/inward/record.url?scp=84906980082&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-10404-1_38
DO - 10.1007/978-3-319-10404-1_38
M3 - Conference contribution
C2 - 25333131
AN - SCOPUS:84906980082
SN - 9783319104034
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 299
EP - 306
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014 - 17th International Conference, Proceedings
PB - Springer Verlag
T2 - 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014
Y2 - 14 September 2014 through 18 September 2014
ER -