Dynamic Multi-Atlas Selection Based Consensus Segmentation of Head and Neck Structures from CT Images.

Dynamic Multi-Atlas Selection Based Consensus Segmentation of Head and Neck Structures from CT Images.:

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Dynamic Multi-Atlas Selection Based Consensus Segmentation of Head and Neck Structures from CT Images.

Med Phys. 2019 Oct 06;:

Authors: Haq R, Berry SL, Deasy JO, Hunt M, Veeraraghavan H

Abstract

PURPOSE: Manual delineation of head and neck (H&N) organ-at-risk (OAR) structures for radiation therapy planning is time consuming and highly variable. Therefore, we developed a dynamic multi-atlas selection-based approach for fast and reproducible segmentation.

METHODS: Our approach dynamically selects and weights the appropriate number of atlases for weighted-label fusion and generates segmentations and consensus maps indicating voxel-wise agreement between different atlases. Atlases were selected for a target as those exceeding an alignment weight called dynamic atlas attention index. Alignment weights were computed at the image-level and called global weighted voting (GWV) or at the structure-level and called structure weighted voting (SWV) by using a normalized metric computed as the sum of squared distances of CT-radiodensity and Modality Independent Neighborhood Descriptors (extracting edge information). Performance comparisons were performed using 77 H&N CT images from an internal Memorial Sloan-Kettering Cancer Center dataset (N=45) and an external dataset (N=32) using Dice Similarity Coefficient (DSC), Hausdorff distance (HD), 95th percentile of HD, Median of Maximum Surface Distance, and Volume Ratio Error against expert delineation. Pair-wise DSC accuracy comparisons of proposed (GWV, SWV) vs. single best atlas (BA) or majority voting (MV) methods were performed using Wilcoxon rank-sum tests.

RESULTS: Both SWV and GWV methods produced significantly better segmentation accuracy than BA (p < 0.001) and MV (p < 0.001) for all OARs within both datasets. SWV generated most accurate segmentations with DSC of: 0.88 for oral cavity, 0.85 for mandible, 0.84 for cord, 0.76 for brainstem and parotids, 0.71 for larynx, and 0.60 for submandibular glands. SWV's accuracy exceeded GWV's for submandibular glands (DSC=0.60 vs 0.52, p=0.019).

CONCLUSIONS: The contributed SWV and GWV methods generated more accurate automated segmentations than the other two MABAS techniques. The consensus maps could be combined with segmentations to visualize voxel-wise consensus between atlases within OARs during manual review.

PMID: 31587300 [PubMed - as supplied by publisher]