Head–neck cancer delineation

4Citations
Citations of this article
14Readers
Mendeley users who have this article in their library.

Abstract

Head–Neck Cancer (HNC) has a relevant impact on the oncology patient population and for this reason, the present review is dedicated to this type of neoplastic disease. In particular, a collection of methods aimed at tumor delineation is presented, because this is a fundamental task to perform efficient radiotherapy. Such a segmentation task is often performed on uni-modal data (usually Positron Emission Tomography (PET)) even though multi-modal images are preferred (PETComputerized Tomography (CT)/PET-Magnetic Resonance (MR)). Datasets can be private or freely provided by online repositories on the web. The adopted techniques can belong to the well-known image processing/computer-vision algorithms or the newest deep learning/artificial intelligence approaches. All these aspects are analyzed in the present review and comparison among various approaches is performed. From the present review, the authors draw the conclusion that despite the encouraging results of computerized approaches, their performance is far from handmade tumor delineation result.

Cite

CITATION STYLE

APA

Faso, E. A. L., Gambino, O., & Pirrone, R. (2021, March 2). Head–neck cancer delineation. Applied Sciences (Switzerland). MDPI AG. https://doi.org/10.3390/app11062721

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free