Pneumothorax Detection Using Spinal Baseline Segmentation
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Solution Overview
Problem
Conventional image analysis systems struggle to accurately predict whether pneumothorax is located in the left or right lung of a patient, which is crucial for emergency tube insertion, especially when patient posture and physical characteristics vary.
Innovation Solution
A pneumothorax detection method and system that uses a trained prediction model to determine the location of pneumothorax and a treated tube by generating a spinal baseline to label left and right regions in images, allowing for accurate classification of emergency pneumothorax and guiding tube insertion treatment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image analysis systems are used to detect pneumothorax, then pneumothorax presence can be identified, but accurate prediction of whether pneumothorax is located in left or right lung is limited
Solution Approach 1:
The patent introduces a spinal baseline as an intermediary reference structure to determine left-right orientation. The spinal baseline serves as a mediator between the image data and the determination of which side is left or right lung, enabling accurate location prediction by first establishing the spinal baseline and then using it to label regions as left or right.
Solution Approach 2:
The patent segments the image into left and right regions based on the spinal baseline. By dividing the image into distinct left and right portions using the spinal reference, the system can accurately determine which pneumothorax detections correspond to left or right lung, improving location prediction accuracy.
2Productivity
If a trained prediction model is used to simultaneously determine pneumothorax presence and location, then emergency treatment can be quickly guided, but the system complexity increases
Solution Approach 1:
The patent merges multiple functions into a single prediction model that simultaneously performs pneumothorax detection, tube detection, and spinal baseline determination. By combining these tasks in one model, the system achieves fast emergency diagnosis without requiring multiple separate processing steps, though the model itself becomes more complex.
Solution Approach 2:
The prediction model is designed with multi-functionality to handle multiple tasks: detecting pneumothorax, detecting tubes, determining spinal baseline, and classifying emergency status. This universal approach allows the single model to perform all necessary functions for emergency pneumothorax diagnosis, improving productivity despite increased model complexity.
Data Source
AI summary
Some embodiments of the present disclosure provide a pneumothorax detection method performed by a computing device. The method may comprise obtaining predicted pneumothorax information, predicted tube information, and a predicted spinal baseline with respect to an input image from a trained pneumothorax prediction model; determining at least one pneumothorax representative position for the predicted pneumothorax information and at least one tube representative position for the predicted tube information, in a prediction image in which the predicted pneumothorax information and the predicted tube information are displayed; dividing the prediction image into a first region and a second region by the predicted spinal baseline; and determining a region in which the at least one pneumothorax representative position and the at least one tube representative position exist among the first region and the second region.