dcm | Digital Color Meter for Windows

I have a DICOM study with 3 series and want to refresh its UIDs (StudyInstanceUID, SeriesInstanceUID, SOPInstanceUID) to do some tests. All the data is in a single directory so it's not possible to tell which DICOM belongs to which series.

What I have tried is using dcmodify (dcmtk) with some generate options :

I used VTK to read a dicom file and I wanted to get its pixel info. The error was caused by the last line. The error info was 'vtkSmartPointerBase::vtkSmartPointerBase(vtkObjectBase *)': cannot convert argument 1 from 'T *' to 'vtkObjectBase *'. But typeid(reader->GetOutput()).name() was actually vtkImageData. Why this error happened?

I have a series of dicom files (.dcm) for each patient that I want to convert to nii.gz files, but nothing happens (even error info) with the following conversion...

I'm currently using the following code to convert DCM slices into NIfti images:

I am trying to create a TensorFlow Dataset from DICOM images using the tf.data API and tensorflow_io, and I want to perform some pre-processing using Hounsfield units from the images. The DICOM images have a shape of (512,512). I have extracted the PixelData from the image and want to convert it to a numpy array of appropriate shape using the following code:

I am trying to create a dummy mask on a sample DICOM Image using pydicom, Opencv and numpy

Getting error -

This is my first question on stack overflow. I apologise in advance for the poor formatting and indentation due to my troubles with the interface.

Environment specifications:

Tensorflow version - 2.7.0 GPU (tested and working properly)

Python version - 3.9.6

CPU - Intel Core i7 7700HQ

GPU - NVIDIA GTX 1060 3GB

RAM - 16GB DDR4 2400MHz

HDD - 1TB 5400 RPM

Problem Statement:

I wish to train a TensorFlow 2.7.0 model to perform multilabel classification with six classes on CT scans stored as DICOM images. The dataset is from Kaggle, link here. The training labels are stored in a CSV file, and the DICOM image names are of the format ID_"random characters".dcm. The images have a combined size of 368 GB.

Approach used:

1. The CSV file containing the labels is imported into a pandas DataFrame and the image filenames are set as the index.

2. A simple data generator is created to read the DICOM image and the labels by iterating on the rows of the DataFrame. This generator is used to create a training dataset using tf.data.Dataset.from_generator. The images are pre-processed using bsb_window().

3. The training dataset is shuffled and split into a training(90%) and validation set(10%)

4. The model is created using Keras Sequential, compiled, and fit using the training and validation datasets created earlier.

code:

I would like to extract from a DICOM all the dicom tags with the same group number. For example, I would like to extract all tags belonging to the group 0010. I tried this, and of course it made an error:

I am having some problems manipulating an answer.

I would like to manipulate a dictionary, because it is simpler for what I need.

To leave it in context basically what I am trying to do is get the status related to the modules that I need.

As you can see it returns the status of many modules, but I need only a few.

This is my code so far: