Hi Thomas,
Current setup.py is difference with requirements.txt
I think should change code as
from setuptools import setup, find_packages

`
import os

with open('./requirements.txt') as f:
requirements = f.read().splitlines()

setup(
name='med_segmentation',
version='1.0.0',
author='MIDAS and kSpace Astronauts',
author_email='[email protected]',
description='Medical Image Segmentation',
long_description=open(os.path.join(os.path.dirname(file), 'README.md')).read(),
package_dir={'med_seg': 'med_seg'},
packages=['med_seg'],
license='public',
keywords='None',
classifiers=[
'Natural Language :: English',
'Programming Language :: Python :: 3 :: Only'
],
install_requires==requirements,
)
`

Hi Thomas,
Thank you for your code.
As I check the code has the model for landmark detection (body identification).
I want to apply this for cardiac (AV, Apex, Aorta root, mitral valve, left/right coronary)
but I don't know how to encode for above code? (is encoded as a mask or as X, Y, Z position?)

Thanks,
Tran

@all-contributors please add @marcfi for code, ideas, maintenance and tool

Hi,
I try to run but get the error with missing function get_fixed_patches_index.
As I check pipeline.py and get_pad_and_patch.py is idendical.
Thanks.
Tran

I would like to use your trained model over my dataset. Would it be possible to use your DL model as a pre-trained model and do a transfer learning?

Hi, thanks for sharing this work. I cloned this repository and read the paper "Fully automated and standardized segmentation of adipose tissue compartments via deep learning in 3D whole-body MRI of epidemiological cohort studies". Now I am wondering which of the models in ModelSet.py are used in the paper. Which one corresponds to the proposed DCNet and which one corresponds to the reference UNet?

Would it be possible to publish an example case such that we could see how to run this pipeline practically? For example just a folder containing a 3D data set, where when you run the pipeline the segmentation maps are produced? Would help a lot with user adoption.

Hi Thomas,
I don't know why sorted function doesn't work for my dataset as it starts from negative

sorted(dicom_paths, key=lambda dicom_path: pydicom.dcmread(dicom_path).SliceLocation)

So I try by use numpy argsort as below.

    slicelocation = np.zeros(len(dicom_paths))
    # sort as slice location
    for f, i in zip(dicom_paths, range(len(dicom_paths))):
        ds = pydicom.read_file(f)
        if('slicelocation' in ds):	slicelocation[i] = ds.SliceLocation
        else:	slicelocation[i] = ds.ImagePositionPatient[2]
    order = np.argsort(slicelocation)
    dicom_paths= np.array(dicom_paths)[order]

Hello,
is there a pretrained model available? Most people do not have annotated datasets which they could use for training. They just want to generate tissue segmentations on their data. For this purpose a pretrained model which is ready to use would be great.

Hello, when I try to run this line: python3 main.py --preprocess -c config/config_default.yml I get the error

sage: MedSeg [-h] [-e EXP_NAME] [--preprocess PREPROCESS] [--train TRAIN] [--evaluate EVALUATE] [--predict PREDICT] [--restore RESTORE] [-c CONFIG_PATH] [--gpu GPU]
[--gpu_memory GPU_MEMORY] [--calculate_max_shape_only CALCULATE_MAX_SHAPE_ONLY] [--split_only SPLIT_ONLY] [--train_epoch TRAIN_EPOCH] [--filters FILTERS]
[--model_name MODEL_NAME] [--train_batch TRAIN_BATCH] [--dataset DATASET]
MedSeg: error: argument --preprocess: expected one argument

Any idea what is going on?