A PyTorch tool kit for estimating the circular content area in endoscopic footage. The algorithm was developed and tested against the Endoscopic Content Area (ECA) dataset. Both this implementation and the dataset are released alongside our publication:
- Rapid and robust endoscopic content area estimation: A lean GPU-based pipeline and curated benchmark dataset
Charlie Budd, Luis C. Garcia-Peraza-Herrera, Martin Huber, Sebastien Ourselin, Tom Vercauteren.
[ arXiv ] [ publication ]
If you make use of this work, please cite the paper.
For Linux users, to install the latest version, simply run...
pip install torchcontentarea
For Windows users, or if you encounter any issues, try building from source by running...
pip install git+https://github.com/charliebudd/torch-content-area
Note: this will require that you have CUDA installed and that its version matches the version of CUDA used to build your installation of PyTorch.
from torchvision.io import read_image
from torchcontentarea import estimate_area, get_points, fit_area
from torchcontentarea.utils import draw_area, crop_area
# Grayscale or RGB image in NCHW or CHW format. Values should be normalised
# between 0-1 for floating point types and 0-255 for integer types.
image = read_image("my_image.png")
# Either directly estimate area from image...
area = estimate_area(image, strip_count=16)
# ...or get the set of points and then fit the area.
points = get_points(image, strip_count=16)
area = fit_area(points, image.shape[2:4])
# Utility function are included to help handle the content area...
area_mask = draw_area(area, image)
cropped_image = crop_area(area, image)Performance is measured against the CholecECA subset of the Endoscopic Content Area (ECA) dataset.
Performance Results (handcrafted cuda)...
- Avg Time (NVIDIA GeForce GTX 980 Ti): 0.299 ± 0.042ms
- Avg Error (Hausdorff Distance): 3.618
- Miss Rate (Error > 15): 2.1%
- Bad Miss Rate (Error > 25): 1.1%