Dataset Information

Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies.

ABSTRACT: Lymphomas are the ninth most common malignant neoplasms as of 2020 and the most common blood malignancies in the developed world. There are multiple approaches to lymphoma staging and monitoring, but all of the currently available ones, generally based either on 2-dimensional measurements performed on CT scans or metabolic assessment on FDG PET/CT, have some disadvantages, including high inter- and intraobserver variability and lack of clear cut-off points. The aim of this paper was to present a novel approach to fully automated segmentation of thoracic lymphoma in pediatric patients. Manual segmentations of 30 CT scans from 30 different were prepared by the authors. nnU-Net, an open-source deep learning-based segmentation method, was used for the automatic segmentation. The highest Dice score achieved by the model was 0.81 (SD = 0.17) on the test set, which proves the potential feasibility of the method, albeit it must be underlined that studies on larger datasets and featuring external validation are required. The trained model, along with training and test data, is shared publicly to facilitate further research on the topic.

SUBMITTER: Klimont M

PROVIDER: S-EPMC9963803 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies.

Klimont Michał M Oronowicz-Jaśkowiak Agnieszka A Flieger Mateusz M Rzeszutek Jacek J Juszkat Robert R Jończyk-Potoczna Katarzyna K

Journal of personalized medicine 20230120 2

Lymphomas are the ninth most common malignant neoplasms as of 2020 and the most common blood malignancies in the developed world. There are multiple approaches to lymphoma staging and monitoring, but all of the currently available ones, generally based either on 2-dimensional measurements performed on CT scans or metabolic assessment on FDG PET/CT, have some disadvantages, including high inter- and intraobserver variability and lack of clear cut-off points. The aim of this paper was to present a ...[more]

PMID: 36836418

Similar Datasets

Project description:ObjectiveImaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals.MethodsThe study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as "ground truth" data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software.ResultsModel segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan).ConclusionsThe authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.

Dataset Information

Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies.

Publications

Deep Learning-Based Segmentation and Volume Calculation of Pediatric Lymphoma on Contrast-Enhanced Computed Tomographies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets