Suture Needle Detection in Radiographic Images via Composite Training
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Solution Overview
Problem
Surgical tools, such as gauze and suture needles, are difficult to detect in radiographic images due to staining and posture, leading to potential complications from tools remaining in patients post-surgery.
Innovation Solution
A learning device uses machine learning to construct a trained model for detecting suture needles by combining radiographic images without tools with images of tools in different postures, generating composite images with specific parameters to enhance detection accuracy across various imaging conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If a discriminator is used to detect surgical tools from radiographic images, then detection capability is improved, but detection accuracy deteriorates when tools are in linear posture or stained
Solution Approach 1:
The system performs preliminary detection of linear structures (such as bones, stents, and surgical tools) in the radiographic image. By identifying and marking these linear objects before final analysis, the discriminator can focus on detecting non-linear surgical tools, thereby improving detection accuracy while maintaining detection capability
Solution Approach 2:
The detection process is segmented into multiple stages: first detecting linear structures, then using this information to guide the detection of non-linear surgical tools. This segmentation allows the system to handle different object types with specialized detection strategies, resolving the contradiction between general detection capability and specific detection accuracy
2Device complexity
If machine learning training data includes only typical postures, then model construction is simplified, but detection reliability deteriorates for varied tool postures
Solution Approach 1:
The training data generation process dynamically creates surgical tool images in various postures and positions within the radiographic image. Instead of using static, predefined postures, the system dynamically varies tool orientation, depth, and location to generate diverse training samples, thereby improving detection reliability without excessively complicating model construction
Solution Approach 2:
The system changes multiple parameters of the surgical tool in training images, including rotation angle, position coordinates, and depth information. By systematically varying these parameters to generate diverse training data, the model learns to recognize tools in various postures while keeping the overall construction process manageable through automated parameter manipulation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The solution enables reliable detection of surgical tools in radiographic images, preventing them from being left in patients after surgery by improving detection accuracy regardless of tool posture and imaging conditions.
Implementation Method 1
the processor may combine the radiographic image that does not include the surgical tool and the surgical tool image to derive a composite image
Data Source
AI summary
A processor performs machine learning, which uses a radiographic image that does not include a suture needle as a surgical tool and a surgical tool image that includes the suture needle in a posture different from a linear posture as training data, to construct a trained model for detecting a region of the suture needle from an input radiographic image.