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https://github.com/gradienthealth/gradient_decode_dicom
🏨Gradient Health DICOM operation for Tensorflow
https://github.com/gradienthealth/gradient_decode_dicom
dicom healthcare tensorflow tensorflow-dataset
Last synced: 23 days ago
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🏨Gradient Health DICOM operation for Tensorflow
- Host: GitHub
- URL: https://github.com/gradienthealth/gradient_decode_dicom
- Owner: gradienthealth
- License: apache-2.0
- Created: 2019-07-09T22:55:06.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-07-15T23:37:29.000Z (over 5 years ago)
- Last Synced: 2024-12-20T08:18:08.139Z (about 2 months ago)
- Topics: dicom, healthcare, tensorflow, tensorflow-dataset
- Language: Python
- Homepage:
- Size: 61.5 KB
- Stars: 51
- Watchers: 3
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Gradient Decode Dicom Tensorflow Operation
## Getting started
1. Install the DCMTK toolkit
`sudo apt-get install libdcmtk-dev`
2. Install gradient_decode_dicom
`pip3 install gradient-decode-dicom`
3. [Example Colab notebook](https://colab.research.google.com/drive/1MdjXN3XkYs_mSyVtdRK7zaCbzkjGub_B)
## Documentation
The `gradient_decode_dicom` package has two operations, `decode_dicom_image` which decodes the pixel data from DICOM files, and `decode_dicom_data` which decodes tag information. `tags` contains useful DICOM tags such as `tags.PatientsName`. We borrow the same tag notation from the [`pydicom`](https://pydicom.github.io/) dicom package.
### Getting DICOM Image Data
```python
gradient_decode_dicom.decode_dicom_image(
contents,
color_dim=False,
on_error='skip',
scale='preserve'
dtype=tf.uint16
name=None
)
```
- **`contents`**: A Tensor of type string. 0-D. The byte string encoded DICOM file
- **`color_dim`**: An optional `bool`. Defaults to `False`. If `True`, a third channel will be appended to all images forming a 3-D tensor. A 1024 x 1024 grayscale image will be 1024 x 1024 x 1
- **`on_error`**: Defaults to `skip`. This attribute establishes the behavior in case an error occurs on opening the image or if the output type cannot accomodate all the possible input values. For example if the user sets the output dtype to tf.uint8, but a dicom image stores a tf.uint16 type. `strict` throws an error. `skip` returns a 1-D empty tensor. `lossy` continues with the operation scaling the value via the `scale` attribute.
- **`scale`**: Defaults to `preserve`. This attribute establishes what to do with the scale of the input values. `auto` will autoscale the input values, if the output type is integer, `auto` will use the maximum output scale for example a `uint8` which stores values from [0, 255] can be linearly stretched to fill a `uint16` that is [0,65535]. If the output is float, `auto` will scale to [0,1]. `preserve` keeps the values as they are, an input value greater than the maximum possible output will be clipped.
- **`dtype`**: An optional `tf.DType` from: `tf.uint8, tf.uint16, tf.uint32, tf.uint64, tf.float16, tf.float32, tf.float64`. Defaults to `tf.uint16`.
- **`name`**: A name for the operation (optional).
**Returns**
A `Tensor` of type `dtype` and the shape is determined by the DICOM file.
### Getting DICOM Tag Data
```python
gradient_decode_dicom.decode_dicom_data(
contents,
tags=None,
name=None
)
```
- **`contents`**: A Tensor of type string. 0-D. The byte string encoded DICOM file
- **`tags`**: A Tensor of type `tf.uint32` of any dimension. These `uint32` numbers map directly to DICOM tags
- **`name`**: A name for the operation (optional).
**Returns**
A `Tensor` of type `tf.string` and same shape as `tags`. If a dicom tag is a list of strings, they are combined into one string and seperated by a double backslash `\\`. There is a bug in [DCMTK](https://support.dcmtk.org/docs/) if the tag is a list of numbers, only the zeroth element will be returned as a string.