Objective

2

Nasopharyngeal carcinoma (NPC) is a common head and neck cancer in Southeast Asia and China. In 2018

approximately 129 thousand people were diagnosed with NPC

and approximately 73 thousand people died of it. Radiotherapy has become a standard treatment method for NPC patients. Precise radiotherapy relies on the accurate delineation of tumor targets and organs-at-risk (OARs). In radiotherapy practice

these anatomical structures are usually manually delineated by radiation oncologists on a treatment-planning system (TPS). Manual delineation

however

is a time-consuming and labor-intensive process. It is also a subjective process and

hence

prone to interpractitioner variability. The NPC target segmentation is particularly challenging because of the substantial interpatient heterogeneity in tumor shape and the poorly defined tumor-to-normal tissue interface

resulting in considerable variations in gross tumor volume among physicians. Auto-segmentation methods have the potential to improve the contouring accuracy and efficiency. Different auto-segmentation methods have been reported. Nevertheless

atlas-based segmentation has long computation time and often could not account for large anatomical variations due to the uncertainty of deformable registration. Deep learning has achieved great success in computer science. It has been applied in auto-segmenting tumor targets and OARs in radiotherapy. Studies have demonstrated that the deep leaning method can perform comparably with or even better than manual segmentation for some tumor sites. In this work

we propose a Deeplabv3+ model that can automatically segment high-risk primary tumor gross target volume (GTVp) in NPC radiotherapy.

Method

2

The Deeplabv3+ convolutional neural network model uses an encoder-decoder structure and a spatial pyramid pooling module to complete the segmentation of high-risk primary tumor from NPC patients. The improved MobileNetV2 network is used as the network backbone

and atrous and depthwise separable convolutions are used in the encoder and decoder modules. The MobileNetV2 network consists of four inverted residual modules that contain depthwise separable convolution with striding to extract feature maps at arbitrary resolutions via atrous separable convolution. Batch normalization and ReLU activation are added after each 3×3 depthwise convolution. The decoder module of this network is as follows: the encoder features are first bilinearly upsampled by a factor of 4 and then concatenated with the corresponding low-level features from the network backbone with the same spatial resolution. We perform a 1×1 convolution on the low-level features to reduce the number of channels. After concatenation

several 3×3 convolutions are used to refine the features

followed by another bilinear upsampling by a factor of 4. Our training and test sets consist of the CT images and manual contours of 150 patients from Anhui Provincial Hospital between January 2016 and May 2019. The dimension

resolution

and thickness of CT images are 512×512

0.98 mm

and 2.5 mm

respectively. To delineate the tumor region efficiently

T1-weighted MR images are also acquired and fused with CT images. GTVp is delineated by experienced radiation oncologists on the CT images in a Pinnacle TPS. Of the 150 patients

120 are chosen as the training set

15 patients are chosen as the validation set

and the remaining 15 patients are chosen as the test set. Images are flipped

translated

and randomly rotated to augment the training dataset. Our network is implemented in Keras toolbox. The input images and ground-truth contours are resized to 512×512 for training. The loss function used in this study is 1-DSC index

AdamOptimizer is used with a learning rate of 0.005

and the weight decay factor is 0.8. The performance of the auto-segmentation algorithm is evaluated with Dice similarity coefficient (DSC)

Jaccard index (JI)

average surface distance (ASD)

and Hausdorff distance (HD). The results are compared with those of the U-Net model. Paired t-test is performed to compare the DSC

JI

ASD

and HD values between the different models.

Result

2

The mean DSC value of the 15 NPC patients from the test set is 0.76±0.11

the mean JI value is 0.63±0.13

the average ASD value is (3.4±2.0) mm

and the average HD value is (10.9±8.6) mm. Compared with the U-Net model

the Deeplabv3+ network model shows improved mean DSC and JI values by 3% and 4%

respectively (0.76±0.11 vs. 0.73±0.13

p

<

0.001; 0.63±0.13 vs. 0.59±0.14

p

<

0.001). The mean ASD value is also significantly reduced (3.4±2.0 vs. 3.8±3.3 mm

p

=0.014) compared with the U-Net result. However

for HD values

no statistical difference exists between the two network models (10.9±8.6 vs. 11.1±7.5 mm

p

=0.745). The experiment results indicate that the Deeplabv3+ network model outperforms the U-Net model in the segmentation of NPC target area. As 2D visualizations of auto-segmented contours

the Deeplabv3+ model results have more overlap with the manual contours and are closer to the results of the "ground truth". The visualizations show that our model can produce refined results. In addition

the average time required to segment a CT image is 16 and 14 ms for our model and the U-Net model

respectively

which is much less than the manual contouring time.

Conclusion

2

In this study

a Deeplabv3+ convolutional neural network model is proposed to auto-segment the GTVp of NPC patients with radiotherapy. The results show that the auto-segmentations of the Deeplabv3+ network are close to the manual contours from oncologists. This model has the potential to improve the efficiency and consistency of GTVp contouring for NPC patients.