Evaluation of localized region-based segmentation algorithms for CT-based delineation of organs at risk in radiotherapy

Abstract

Background and purpose: In radiation therapy, defining the precise borders of cancerous tissues and adjacent normal organs has a significant effect on the therapy outcome. Deformable models offer a unique and robust approach to medical image segmentation. The objective of this study was to investigate the reliability of segmenting organs-at-risk (OARs) using three well-known local region-based level-set techniques. Methods and materials: A total of 1340 non-enhanced and enhanced planning computed tomography (CT) slices of eight OARs (the bladder, rectum, kidney, clavicle, humeral head, femoral head, spinal cord, and lung) were segmented by using local region-based active contour, local Chan-Vese, and local Gaussian distribution models. Quantitative metrics, namely Hausdorff Distance (HD), Mean Absolute Distance (MAD), Dice coefficient (DC), Percentage Volume Difference (PVD) and Absolute Volumetric Difference (AVD), were adopted to measure the correspondence between detected contours and the manual references drawn by experts. Results: The results showed the feasibility of using local region-based active contour methods for defining six of the OARs (the bladder, kidney, clavicle, humeral head, spinal cord, and lung) when adequate intensity information is available. While the most accurate results were achieved for lung (DC = 0.94) and humeral head (DC = 0.92), a poor level of agreement (DC < 0.7) was obtained for both rectum and femur. Conclusion: Incorporating local statistical information in level set methods yields to satisfactory results of OARs delineation when adequate intensity information exists between the organs. However, the complexity of adjacent organs and the lack of distinct boundaries would result in a considerable segmentation error.

Imaging science and development in modern high-precision radiotherapy

“…Of note, for high quality usage of functional and anatomic imaging data, dedicated robust strategies for image registration [86] , [87] , [88] , [89] and data analysis [90] are needed. Only then, reliable new segmentation algorithms [91] , [92] and prediction models can be trained [16] , [93] .…”

LEFMIS: locally-oriented evaluation framework for medical image segmentation algorithms

“…Juneja et al (2013) proposed to develop the idea of using DSC for inter-observer variability and proposed a validation index (VI). Astaraki et al (2018) used DSC, HD, MAD, absolute volumetric difference and percentage volume difference in the radiotherapy context. Three segmentation algorithms for the CT-based delineation of the organs at risk were compared.…”

Prior Attention Enhanced Convolutional Neural Network Based Automatic Segmentation of Organs at Risk for Head and Neck Cancer Radiotherapy

“…Organs at risk (OARs) delineation in computed tomography (CT) is a critical step in radiotherapy planning to achieve organ dose sparing for minimizing radiation-induced toxicity [1,2]. Manual delineation is usually adopted in current clinical practices, which is time-consuming and with large inter-and intra-operator variabilities [3].…”