Dynamic Multiatlas Selection-based Consensus Segmentation of Head and Neck Structures from CT Images

Overview

Journal Med Phys

Specialty Biophysics

Date 2019 Oct 7

PMID 31587300

Citations 9

Authors

Rabia Haq

Sean L Berry

Joseph O Deasy

Margie Hunt

Harini Veeraraghavan

Affiliations

Soon will be listed here.

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 multiatlas 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 computed tomography (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. Pairwise 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 the 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 multiatlas-based segmentation techniques. The consensus maps could be combined with segmentations to visualize voxel-wise consensus between atlases within OARs during manual review.

Citing Articles

Generalizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT images.

Guerendel C, Petrychenko L, Chupetlovska K, Bodalal Z, Beets-Tan R, Benson S Eur Radiol. 2024; .

PMID: 39738559 DOI: 10.1007/s00330-024-11321-2.

Deep learning for automatic head and neck lymph node level delineation provides expert-level accuracy.

Weissmann T, Huang Y, Fischer S, Roesch J, Mansoorian S, Ayala Gaona H Front Oncol. 2023; 13:1115258.

PMID: 36874135 PMC: 9978473. DOI: 10.3389/fonc.2023.1115258.

Automated segmentation of the larynx on computed tomography images: a review.

Rao D, K P, Singh R, J V Biomed Eng Lett. 2022; 12(2):175-183.

PMID: 35529346 PMC: 9046475. DOI: 10.1007/s13534-022-00221-3.

One shot PACS: Patient specific Anatomic Context and Shape prior aware recurrent registration-segmentation of longitudinal thoracic cone beam CTs.

Jiang J, Veeraraghavan H IEEE Trans Med Imaging. 2022; PP.

PMID: 35213307 PMC: 9642320. DOI: 10.1109/TMI.2022.3154934.

Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review.

Qiu B, van der Wel H, Kraeima J, Glas H, Guo J, Borra R J Pers Med. 2021; 11(7).

PMID: 34357096 PMC: 8307673. DOI: 10.3390/jpm11070629.

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