Impact of the Intra- and Inter-observer Variability in the Delineation of Parotid Glands on the Dose Calculation During Head and Neck Helical Tomotherapy

AbstractAdaptive online MRI-guided radiotherapy of head and neck cancer requires the reliable segmentation of the parotid glands as important organs at risk in clinically acceptable time frames. This can hardly be achieved by manual contouring. We therefore designed deep learning-based algorithms which automatically perform this task.Imaging data comprised two datasets: 27 patient MR images (T1-weighted and T2-weighted) and eight healthy volunteer MR images (T2-weighted), together with manually drawn contours by an expert. We used four different convolutional neural network (CNN) designs that each processed the data differently, varying the dimensionality of the input. We assessed the segmentation accuracy calculating the Dice similarity coefficient (DSC), Hausdorff distance (HD) and mean surface distance (MSD) between manual and auto-generated contours. We benchmarked the developed methods by comparing to the inter-observer variability and to atlas-based segmentation. Additionally, we assessed the generalisability, strengths and limitations of deep learning-based compared to atlas-based methods in the independent volunteer test dataset.With a mean DSC of 0.85± 0.11 and mean MSD of 1.82 ±1.94 mm, a 2D CNN could achieve an accuracy comparable to that of an atlas-based method (DSC: 0.85 ±0.05, MSD: 1.67 ±1.21 mm) and the inter-observer variability (DSC: 0.84 ±0.06, MSD: 1.50 ±0.77 mm) but considerably faster (<1s v.s. 45 min). Adding information (adjacent slices, fully 3D or multi-modality) did not further improve the accuracy. With additional preprocessing steps, the 2D CNN was able to generalise well for the fully independent volunteer dataset (DSC: 0.79 ±0.10, MSD: 1.72 ±0.96 mm)We demonstrated the enormous potential for the application of CNNs to segment the parotid glands for online MRI-guided radiotherapy. The short computation times render deep learning-based methods suitable for online treatment planning workflows.

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Automatic Parotid Gland Segmentation in MVCT Using Deep Convolutional Neural Networks

ACM Transactions on Computing for Healthcare ◽

10.1145/3485278 ◽

2022 ◽

Vol 3 (2) ◽

pp. 1-15

Author(s):

Junqian Zhang ◽

Yingming Sun ◽

Hongen Liao ◽

Jian Zhu ◽

Yuan Zhang

Keyword(s):

Parotid Gland ◽

Head And Neck ◽

Helical Tomotherapy ◽

Radiation Treatment ◽

Automatic Segmentation ◽

Parotid Glands ◽

Deep Convolutional Neural Networks ◽

Megavoltage Computed Tomography ◽

Radiation Induced ◽

Segmentation Models

Radiation-induced xerostomia, as a major problem in radiation treatment of the head and neck cancer, is mainly due to the overdose irradiation injury to the parotid glands. Helical Tomotherapy-based megavoltage computed tomography (MVCT) imaging during the Tomotherapy treatment can be applied to monitor the successive variations in the parotid glands. While manual segmentation is time consuming, laborious, and subjective, automatic segmentation is quite challenging due to the complicated anatomical environment of head and neck as well as noises in MVCT images. In this article, we propose a localization-refinement scheme to segment the parotid gland in MVCT. After data pre-processing we use mask region convolutional neural network (Mask R-CNN) in the localization stage after data pre-processing, and design a modified U-Net in the following fine segmentation stage. To the best of our knowledge, this study is a pioneering work of deep learning on MVCT segmentation. Comprehensive experiments based on different data distribution of head and neck MVCTs and different segmentation models have demonstrated the superiority of our approach in terms of accuracy, effectiveness, flexibility, and practicability. Our method can be adopted as a powerful tool for radiation-induced injury studies, where accurate organ segmentation is crucial.

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Investigation of Autosegmentation Techniques on T2-Weighted MRI for Off-line Dose Reconstruction in MR-Linac Adapt to Position Workflow for Head and Neck Cancers

10.1101/2021.09.30.21264327 ◽

2021 ◽

Author(s):

Brigid A McDonald ◽

Carlos Cardenas ◽

Nicolette O'Connell ◽

Sara Ahmed ◽

Mohamed A. Naser ◽

...

Keyword(s):

Head And Neck ◽

Execution Time ◽

Organs At Risk ◽

Ground Truth ◽

Dice Similarity Coefficient ◽

Observer Variability ◽

Dose Reconstruction ◽

Inter Observer Variability ◽

Significant Difference ◽

Delivered Dose

Purpose: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. In this study, our goal is to evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. Methods: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. 20 autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior (IPP)) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance, Hausdorff distance, and Jaccard index. For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions (IPP_RF_4), IPP with 1 fraction (IPP_1)), and one low-performing (PAL with STAPLE and 5 atlases (PAL_ST_5)). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. Results: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 seconds per case) and PAL methods the slowest (3.7 - 13.8 minutes per case). Execution time increased with number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). Conclusions: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

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Impact of interobserver variability in delineating clinical target volumes (CTV) for head and neck intensity modulated radiation therapy (IMRT): Potential for a geographical miss

Journal of Clinical Oncology ◽

10.1200/jco.2009.27.15_suppl.e17013 ◽

2009 ◽

Vol 27 (15_suppl) ◽

pp. e17013-e17013

Author(s):

S. Chilukuri ◽

S. Surana ◽

P. P. Mohanty ◽

R. Kuppuswamy

Keyword(s):

Head And Neck Cancer ◽

Head And Neck ◽

Neck Cancer ◽

Imaging Techniques ◽

Target Volume ◽

High Dose ◽

Observer Variability ◽

Median Dose ◽

Median Percentage ◽

Inter Observer Variability

e17013 Background: Despite modern day imaging techniques and guidelines for delineation of the clinical target volume, there remains significant inter-observer variability in delineating the CTV. With the use of IMRT, the target volume receives a significant tumoricidal dose while the regions just outside the target receive unpredictable doses. In this report, the dose to the region just outside the planning target volume (PTV) (defined as volume of uncertainty [VOU]), presumed to represent the regions subject to maximum inter-observer variability, was studied. Methods: The IMRT plans of 12 patients with head and neck cancer were used to determine the dose just outside the high-risk CTV by growing volumes around CTV with 3 mm, 5 mm, and 7 mm margins. These volumes were edited at regions close to skin/air and bone. PTVs were subsequently grown using the same margins as used in the original plans. With the Boolean operations, each of these volumes was subtracted from the existing PTV to generate the volumes of uncertainty (VOU) in 3 dimensions. The dose to these VOUs was analyzed. D95, D90 and median dose which are the doses received by 95%, 90%, and 50% of the target volume respectively were studied. Results: The median prescribed dose was 68 Gy (60 Gy-72 Gy). The median percentage D95 for 3mm, 5mm and 7mm VOU was 82.5% ± 4.95, 77.25% ± 5.53, and 69% ± 6.93, respectively. The median percentage D90 for these VOU's was 87.7% ± 3.53, 83.2% ± 4.61, and 79% ± 4.5, respectively. The median dose to each of these VOU”s was 96% ± 1.6, 94.5% ± 1.95, and 92.5% ± 1.85 respectively. Conclusions: This study documents that the volumes of uncertainty surrounding the PTV, which could contain subclinical disease, in fact receive a significant amount of RT dose. Hence, despite a large amount of evidence for inter-observer variability in target delineation for head and neck cancer,the majority of locoregional recurrences are within the high dose region and not marginal failures. No significant financial relationships to disclose.

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