Comments (6)
Thank you for your question! This is indeed a more challenging case with liver metastases that have relatively low tracer uptake compared to the liver background. Still, these are metastases and should - in principle - be identified. Admittedly, this case might be challenging even for human experts.
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Hi @sergiosgatidis ,
Thanks for your answer very much.
Would it be possible to provide more details on the annotation protocol? E.g., how do the radiologists identify the lesions from PET and CT, and how do they avoid FP and FN based on the images?
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The annotation process relies on many years of experience in clinical hybrid imaging combined with relevant clinical knowledge. We will soon publish a technical paper describing the dataset and the annotation process in detail. Once published we will post it on the TCIA page.
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Hi @sergiosgatidis ,
Would it be possible for you to share some general protocols? how do the radiologists identify the lesions from PET and CT, and how do they avoid FP and FN based on the images?
Since the testing submission deadline is very close, this information is very important for us to select postprocessing strategies.
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A very good source that describes this process can be found here: https://cancerimagingjournal.biomedcentral.com/articles/10.1186/s40644-016-0091-3
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Awesome!
Thanks:)
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Related Issues (12)
- LFS bandwidth HOT 1
- Clone error
- PermissionError: [Errno 13] Permission denied: '/input/images/ct/' HOT 16
- Issues in process.py file HOT 1
- Unpickling error while running run_inference function HOT 2
- Unable to determine ImageIO reader for "/input/images/pet/e260efef-0a29-4c68-972e-9e573c740de5.mha" HOT 8
- Additional information on unet baseline HOT 7
- shared memory size at docker run time HOT 4
- About the dataset HOT 1
- Unable to load the ckpt file HOT 2
- Question regarding running predictions on new data HOT 3
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