scholarly journals Auto-segmentation of the parotid glands on MR images of head and neck cancer patients with deep learning strategies

Author(s):  
Jennifer P. Kieselmann ◽  
Clifton D. Fuller ◽  
Oliver J. Gurney-Champion ◽  
Uwe Oelfke

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.

2009 ◽  
Vol 27 (15_suppl) ◽  
pp. e17013-e17013
Author(s):  
S. Chilukuri ◽  
S. Surana ◽  
P. P. Mohanty ◽  
R. Kuppuswamy

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.


2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Bin Huang ◽  
Zhewei Chen ◽  
Po-Man Wu ◽  
Yufeng Ye ◽  
Shi-Ting Feng ◽  
...  

Purpose. In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods. PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results. A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P<0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion. A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.


2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Enis Tinjak ◽  
Velda Smajlbegović ◽  
Adnan Beganović ◽  
Mirjana Ristanić ◽  
Halil Ćorović ◽  
...  

Introduction: Radiation therapy has long played an integral role in the manage¬ment of locally advanced head and neck cancer (HNC), both for organ preservation and to improve tumor control in the postoperative setting. The aim of this research is to investigate the effects of adaptive radiotherapy on dosimetric, clinical, and toxicity outcomes for patients with head and neck cancer undergoing radiation therapy treatment. Many sources have reported volume reductions in the primary target, nodal volumes, and parotid glands over treatment, which may result in unintended dosimetric changes affecting the side effect profile and even efficacy of the treatment. Adaptive radiotherapy (ART) is an interesting treatment paradigm that has been developed to directly adjust to these changes.Material and methods: This research contains the results of 15 studies, including clinical trials, randomized prospective and retrospective studies. The researches analyze the impact of radiation therapy on changes in tumor volume and the relationship with planned radiation dose delivery, as well as the possibility of using adaptive radiotherapy in response to identified changes. Also, medical articles and abstracts that are closely related to the title of adaptive radiotherapy were researched.Results: The application of ART significantly improved the quality of life of patients with head and neck cancer, as well as two-year locoregional control of the disease. The average time to apply ART is the middle of the treatment course approximately 17 to 20 fractions of the treatment.Conclusion: Based on systematic review of the literature, evidence based changes in target volumes and dose reduction at OAR, adaptive radiotherapy is recommended treatment for most of the patients with head and neck cancer with the support of image-guided radiotherapy.


2021 ◽  
Author(s):  
Brigid A McDonald ◽  
Carlos Cardenas ◽  
Nicolette O'Connell ◽  
Sara Ahmed ◽  
Mohamed A. Naser ◽  
...  

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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