Abstract
Background Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. The aim of this study is to introduce and evaluate an automatic segmentation pipeline for HNSCC using a multi-view CNN (MV-CNN).MethodsThe dataset included 220 patients with primary HNSCC and availability of T1-weighted, STIR and optionally contrast-enhanced T1-weighted MR images together with a manual reference segmentation of the primary tumor by an expert. A T1-weighted standard space of the head and neck region was created to register all MRI sequences to. An MV-CNN was trained with these three MRI sequences and evaluated in terms of volumetric and spatial performance in a cross-validation by measuring intra-class correlation (ICC) and dice similarity score (DSC), respectively.ResultsThe average manual segmented primary tumor volume was 11.8±6.70 cm3 with a median [IQR] of 13.9 [3.22-15.9] cm3. The tumor volume measured by MV-CNN was 22.8±21.1 cm3 with a median [IQR] of 16.0 [8.24-31.1] cm3. Compared to the manual segmentations, the MV-CNN scored an average ICC of 0.64±0.06 and a DSC of 0.49±0.19. Improved segmentation performance was observed with increasing primary tumor volume: the smallest tumor volume group (<3 cm3) scored a DSC of 0.26±0.16 and the largest group (>15 cm3) a DSC of 0.63±0.11 (p<0.001). The automated segmentation tended to overestimate compared to the manual reference, both around the actual primary tumor and in false positively classified healthy structures and pathologically enlarged lymph nodes.ConclusionAn automatic segmentation pipeline was evaluated for primary HNSCC on MRI. The MV-CNN produced reasonable segmentation results, especially on large tumors, but overestimation decreased overall performance. In further research, the focus should be on decreasing false positives and make it valuable in treatment planning.
Highlights
Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning
MV-Convolutional neural network (CNN) showed a structural overestimation of the predicted tumor volume (Fig. 3A)
Misclassifications of the automatic segmentation often occurred in pathologically enlarged lymph nodes, there was no difference found in model performance between cases from different lymph node subgroups in the TNM classification (Table 2)
Summary
Accurate segmentation of head and neck squamous cell cancer (HNSCC) is important for radiotherapy treatment planning. Manual segmentation of these tumors is time-consuming and vulnerable to inconsistencies between experts, especially in the complex head and neck region. Accurate primary tumor delineation is a crucial step in radiotherapy planning and is performed manually or semi-automatically by radiation oncologists [4]. This process is often time consuming and inconsistencies between experts can have significant influence on precision of the treatment [5, 6]. Multi-view convolutional neural networks (MV-CNNs) have been successfully used in a variety of segmentation tasks on medical datasets, where three identical networks are trained simultaneously each on a different 2D plane so that information is included from three planes without the computational complexity of 3D patches [14,15,16]
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