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Error analysis driven network modification for surgical tools detection in laparoscopic frames
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DOI：10.1002/ima.22791.png)
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Abstract
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In minimally invasive laparoscopic surgery, accurate detection of the location and specific category of surgical tools assists the surgeon in making a correct objective judgment of the current surgical outcome and alerts to these possible adverse medical events. In this paper, an analyzing error method is proposed for revealing the specific sources of error in previous work. Then we confirm the main bottlenecks of the current methods with a high level of missing error and localization error. To reduce these errors, we render the backbone with a three-dimensional attention mechanism and adopt a double-headed detection head design to replace the single detection head. Moreover, we propose an enhanced multilayer perceptron called Mconv to enhance the localization branch of the double-headed detection head. Our experiments evaluate our improved approach by our proposed error analysis method on a pubic dataset m2cai16-tool-locations, showing that our method yield remarkable detection accuracy than others.
Keywords:
deep learning
error analysis
laparoscopic surgery
tool detection
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Biochemistry
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