Loading [a11y]/accessibility-menu.js
Evaluating the Segmentation Performance of Gross Volume Tumor in Cervical Cancer Using MRI Images | IEEE Conference Publication | IEEE Xplore

Evaluating the Segmentation Performance of Gross Volume Tumor in Cervical Cancer Using MRI Images


Abstract:

Cervical Cancer (CC) is the most prevalent gynecologic malignancy worldwide. Tumor segmentation in CC is a crucial step for radiotherapy treatment and planning. The clini...Show More

Abstract:

Cervical Cancer (CC) is the most prevalent gynecologic malignancy worldwide. Tumor segmentation in CC is a crucial step for radiotherapy treatment and planning. The clinical practice involves laborious slice-by-slice segmentation of the primary tumor using simultaneous assessments from several image modalities, and ignores spatial ambiguity in tumor delineation. This work evaluates the performance of a state-of-the-art Landing AI for automated 3D medical image segmentation applied to Gross Tumor Volume (GTV) in CC from Magnetic Resonance Imaging (MRI) scans. Our work provides a novel in-house labeled dataset with a systematic assessment of the segmented lesions after network training on various voxel spacing MRI images. The segmentation performance was assessed using the dice coefficient. We demonstrated that training on MRI images to optimize Landing AI achieves an improved dice score of 0.92, outperforming other MRI models.
Date of Conference: 27-29 August 2024
Date Added to IEEE Xplore: 08 October 2024
ISBN Information:

ISSN Information:

Conference Location: Natal, Brazil

References

References is not available for this document.