Abstract
BackgroundAutomatic tumor segmentation based on Convolutional Neural Networks (CNNs) has shown to be a valuable tool in treatment planning and clinical decision making. We investigate the influence of 7 MRI input channels of a CNN with respect to the segmentation performance of head&neck cancer.MethodsHead&neck cancer patients underwent multi-parametric MRI including T2w, pre- and post-contrast T1w, T2*, perfusion (ktrans, ve) and diffusion (ADC) measurements at 3 time points before and during radiochemotherapy. The 7 different MRI contrasts (input channels) and manually defined gross tumor volumes (primary tumor and lymph node metastases) were used to train CNNs for lesion segmentation. A reference CNN with all input channels was compared to individually trained CNNs where one of the input channels was left out to identify which MRI contrast contributes the most to the tumor segmentation task. A statistical analysis was employed to account for random fluctuations in the segmentation performance.ResultsThe CNN segmentation performance scored up to a Dice similarity coefficient (DSC) of 0.65. The network trained without T2* data generally yielded the worst results, with ΔDSCGTV-T = 5.7% for primary tumor and ΔDSCGTV-Ln = 5.8% for lymph node metastases compared to the network containing all input channels. Overall, the ADC input channel showed the least impact on segmentation performance, with ΔDSCGTV-T = 2.4% for primary tumor and ΔDSCGTV-Ln = 2.2% respectively.ConclusionsWe developed a method to reduce overall scan times in MRI protocols by prioritizing those sequences that add most unique information for the task of automatic tumor segmentation. The optimized CNNs could be used to aid in the definition of the GTVs in radiotherapy planning, and the faster imaging protocols will reduce patient scan times which can increase patient compliance.Trial registrationThe trial was registered retrospectively at the German Register for Clinical Studies (DRKS) under register number DRKS00003830 on August 20th, 2015.
Highlights
Head&neck squamous cell carcinomas (HNSCC) are currently treated with surgery, chemotherapy, radiation therapy or a combination thereof [1, 2] such as primary radio-chemotherapy
Magnetic resonance imaging (MRI) is often used for the gross tumor volume (GTV) target volume, as it provides superior soft tissue contrast compared to Computed tomography (CT) among other benefits [5, 6]
To overcome this bias and to accelerate the radiation planning procedure, in recent years automatic tumor segmentation methods have been introduced. These segmentation methods are based on deep learning techniques such as convolutional neural networks (CNN) that have been shown to be highly accurate in the segmentation of various tumor types [9,10,11,12], and promise to be a valuable tool in assisting experts in clinical decision making
Summary
Head&neck squamous cell carcinomas (HNSCC) are currently treated with surgery, chemotherapy, radiation therapy or a combination thereof [1, 2] such as primary radio-chemotherapy. Manual GTV definition is a tedious and time-consuming procedure which can require up to 2 h per patient, and which is strongly dependent on the training of the executing radio-therapist [7, 8] To overcome this bias and to accelerate the radiation planning procedure, in recent years automatic tumor segmentation methods have been introduced. These segmentation methods are based on deep learning techniques such as convolutional neural networks (CNN) that have been shown to be highly accurate in the segmentation of various tumor types [9,10,11,12], and promise to be a valuable tool in assisting experts in clinical decision making. We investigate the influence of 7 MRI input channels of a CNN with respect to the segmentation performance of head&neck cancer
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