Development and Practical Implementation of a Deep Learning-Based Pipeline for Automated Pre- and Postoperative Glioma Segmentation.
AJNR Am J Neuroradiol
; 43(1): 24-32, 2022 01.
Article
en En
| MEDLINE
| ID: mdl-34857514
ABSTRACT
BACKGROUND AND PURPOSE:
Quantitative volumetric segmentation of gliomas has important implications for diagnosis, treatment, and prognosis. We present a deep-learning model that accommodates automated preoperative and postoperative glioma segmentation with a pipeline for clinical implementation. Developed and engineered in concert, the work seeks to accelerate clinical realization of such tools. MATERIALS ANDMETHODS:
A deep learning model, autoencoder regularization-cascaded anisotropic, was developed, trained, and tested fusing key elements of autoencoder regularization with a cascaded anisotropic convolutional neural network. We constructed a dataset consisting of 437 cases with 40 cases reserved as a held-out test and the remainder split 8020 for training and validation. We performed data augmentation and hyperparameter optimization and used a mean Dice score to evaluate against baseline models. To facilitate clinical adoption, we developed the model with an end-to-end pipeline including routing, preprocessing, and end-user interaction.RESULTS:
The autoencoder regularization-cascaded anisotropic model achieved median and mean Dice scores of 0.88/0.83 (SD, 0.09), 0.89/0.84 (SD, 0.08), and 0.81/0.72 (SD, 0.1) for whole-tumor, tumor core/resection cavity, and enhancing tumor subregions, respectively, including both preoperative and postoperative follow-up cases. The overall total processing time per case was â¼10 minutes, including data routing (â¼1 minute), preprocessing (â¼6 minute), segmentation (â¼1-2 minute), and postprocessing (â¼1 minute). Implementation challenges were discussed.CONCLUSIONS:
We show the feasibility and advantages of building a coordinated model with a clinical pipeline for the rapid and accurate deep learning segmentation of both preoperative and postoperative gliomas. The ability of the model to accommodate cases of postoperative glioma is clinically important for follow-up. An end-to-end approach, such as used here, may lead us toward successful clinical translation of tools for quantitative volume measures for glioma.
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Aprendizaje Profundo
/
Glioma
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
AJNR Am J Neuroradiol
Año:
2022
Tipo del documento:
Article