Application of FGD-BCEL loss function in segmenting temporal lobes on localized CT images for radiotherapy.

ABSTRACT:

Objectives

The aim of this study was to find a new loss function to automatically segment temporal lobes on localized CT images for radiotherapy with more accuracy and a solution to dealing with the classification of class-imbalanced samples in temporal lobe segmentation.

Methods

Localized CT images for radiotherapy of 70 patients with nasopharyngeal carcinoma were selected. Radiation oncologists sketched mask maps. The dataset was randomly divided into the training set (n = 49), the validation set (n = 7), and the test set (n = 14). The training set was expanded by rotation, flipping, zooming, and shearing, and the models were evaluated using Dice similarity coefficient (DSC), Jaccard similarity coefficient (JSC), positive predictive value (PPV), sensit

SUBMITTER: Wen X 

PROVIDER: S-EPMC10585164 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature