Automated segmentation of the larynx on computed tomography images: a review

Abstract

The larynx, or the voice-box, is a common site of occurrence of Head and Neck cancers. Yet, automated segmentation of the larynx has been receiving very little attention. Segmentation of organs is an essential step in cancer treatment-planning. Computed Tomography scans are routinely used to assess the extent of tumor spread in the Head and Neck as they are fast to acquire and tolerant to some movement. This paper reviews various automated detection and segmentation methods used for the larynx on Computed Tomography images. Image registration and deep learning approaches to segmenting the laryngeal anatomy are compared, highlighting their strengths and shortcomings. A list of available annotated laryngeal computed tomography datasets is compiled for encouraging further research. Commercial software currently available for larynx contouring are briefed in our work. We conclude that the lack of standardisation on larynx boundaries and the complexity of the relatively small structure makes automated segmentation of the larynx on computed tomography images a challenge. Reliable computer aided intervention in the contouring and segmentation process will help clinicians easily verify their findings and look for oversight in diagnosis. This review is useful for research that works with artificial intelligence in Head and Neck cancer, specifically that deals with the segmentation of laryngeal anatomy.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-022-00221-3.

Keywords: Artificial Intelligence; Computed Tomography; Computer-Aided Detection; Larynx Segmentation; Medical Image Processing.

Fig. 1

Laryngeal anatomy and its subsites [5]

Fig. 2

Laryngeal tumor delineated on (a) contrast-enhanced CT, (b) MRI and (c) PET CT [8]

Fig. 3

Illustration of Dice Coefficient

Fig. 4

Atlas-based registration of laryngeal anatomic substructures [16]

Fig. 5

CNN architecture

Fig. 6

Rimaglottidis segmentation output [32]