medicapture / seg-model

Hip segmentation from the X-ray image

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  • 307 runs
  • Paper
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Run time and cost

This model costs approximately $0.0014 to run on Replicate, or 714 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 1 seconds. The predict time for this model varies significantly based on the inputs.

Readme

We segment the pelvis part with U-Net++, which is an improved version of U-Net. We use X-ray images of the pelvis to train our model, with 191 images as training data, and 33 images as validation data. Pixels in each image are labeled with two classes, where label 0 is background, and label 1 is pelvis. The model outputs a probability map with the size (N, H, W), where N is the number of classes; H and W are the height and width of input image. Each pixel represents the class prediction.

The probability map prediction result of our data. (a) X-ray image before surgery. (b) X-ray image after surgery. (c) Probability map prediction of pelvis before surgery. (d) After surgery. The pixel value represents the confidence score of pelvis.