Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images

TL;DR

This paper introduces a novel deep learning model that assists in slice-by-slice adaptive segmentation of oropharyngeal cancer tumors using FDG PET/CT images, accounting for spatial uncertainty to improve radiotherapy planning.

Contribution

It presents a new DL framework that integrates multi-plane context and adaptive thresholding for more accurate tumor segmentation in PET/CT images.

Findings

Achieved a mean Dice score of up to 0.80 at a probability threshold of 0.9.

Model predictions closely matched ground truth contours, aiding clinical decision-making.

Demonstrated the effectiveness of multi-plane analysis in tumor delineation.

Abstract

Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oropharyngeal cancer patients (OPC), simultaneous assessment of different image modalities is needed, and each image volume is explored slice-by-slice from different orientations. Moreover, the manual fixed boundary of segmentation neglects the spatial uncertainty known to occur in tumor delineation. This study proposes a novel automatic deep learning (DL) model to assist radiation oncologists in a slice-by-slice adaptive GTVp segmentation on registered FDG PET/CT images. We included 138 OPC patients treated with (chemo)radiation in our institute. Our DL framework exploits both inter and intra-slice context. Sequences of 3 consecutive 2D slices of concatenated FDG PET/CT images and GTVp contours were used as input. A 3-fold cross validation was performed three times, training on sequences extracted from the Axial (A), Sagittal (S), and Coronal (C) plane of 113 patients. Since consecutive sequences in a volume contain overlapping slices, each slice resulted in three outcome predictions that were averaged. In the A, S, and C planes, the output shows areas with different probabilities of predicting the tumor. The performance of the models was assessed on 25 patients at different probability thresholds using the mean Dice Score Coefficient (DSC). Predictions were the closest to the ground truth at a probability threshold of 0.9 (DSC of 0.70 in the A, 0.77 in the S, and 0.80 in the C plane). The promising results of the proposed DL model show that the probability maps on registered FDG PET/CT images could guide radiation oncologists in a slice-by-slice adaptive GTVp segmentation.