An Enhanced Mask R-CNN Approach for Pulmonary Embolism Detection and Segmentation.
Diagnostics (Basel)
; 14(11)2024 May 26.
Article
in En
| MEDLINE
| ID: mdl-38893629
ABSTRACT
Pulmonary embolism (PE) refers to the occlusion of pulmonary arteries by blood clots, posing a mortality risk of approximately 30%. The detection of pulmonary embolism within segmental arteries presents greater challenges compared with larger arteries and is frequently overlooked. In this study, we developed a computational method to automatically identify pulmonary embolism within segmental arteries using computed tomography (CT) images. The system architecture incorporates an enhanced Mask R-CNN deep neural network trained on PE-containing images. This network accurately localizes pulmonary embolisms in CT images and effectively delineates their boundaries. This study involved creating a local data set and evaluating the model predictions against pulmonary embolisms manually identified by expert radiologists. The sensitivity, specificity, accuracy, Dice coefficient, and Jaccard index values were obtained as 96.2%, 93.4%, 96.%, 0.95, and 0.89, respectively. The enhanced Mask R-CNN model outperformed the traditional Mask R-CNN and U-Net models. This study underscores the influence of Mask R-CNN's loss function on model performance, providing a basis for the potential improvement of Mask R-CNN models for object detection and segmentation tasks in CT images.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
Diagnostics (Basel)
Year:
2024
Document type:
Article
Affiliation country:
Turquía
Country of publication:
Suiza