Deep Learning-Based Standard Section Recognition and Multi-Organ Segmentation in Upper Abdominal Ultrasound.

OBJECTIVE

To develop and evaluate deep learning models for upper abdominal ultrasound standard section recognition and downstream multi-organ segmentation.

METHODS

This retrospective study used real-world clinical ultrasound data from a single center. A total of 521 upper abdominal ultrasound examination videos were collected, of which 465 met the inclusion criteria. Standard section frames were extracted and reviewed by sonographers, yielding 5535 images representing 12 clinically defined upper abdominal standard sections across the liver, gallbladder, pancreas, and spleen. A two-stage sequential framework was implemented, in which standard section recognition using a convolutional neural network served as a prerequisite screening step, followed by organ segmentation using a deep learning-based segmentation network applied only to recognized standard sections. Data were divided into training, validation, and test sets with strict case-level separation.

RESULTS

The standard section recognition model demonstrated stable performance across datasets, achieving Precision 97.08%, Recall 97.00%, Specificity 99.70%, and Accuracy 97.00% on the test set. Organ segmentation performed under standard section constraints yielded strong results for the liver, gallbladder, and right kidney, whereas performance was lower for the spleen and particularly the pancreas (test IoU 53.76%). Performance patterns were consistent across the validation and test subsets.

CONCLUSION

Deep learning models for standard section recognition and multi-organ segmentation are feasible within this single-center real-world dataset and address two complementary tasks in upper abdominal ultrasound; external validation is still needed to determine broader generalizability.