To run these examples, you’ll need to install external deid-data.

$ pip install deid-data

Get Identifiers

A get request using the deid module will return a data structure with headers found in a particular dataset. Let’s walk through these steps. As we did in the loading, the first step was to load a dicom dataset:

from import get_dataset
from deid.dicom import get_files

base = get_dataset("dicom-cookies")
dicom_files = list(get_files(base))

We now have our small dataset that we want to de-identify! The first step is to get the identifiers. By default, we will return all of them. That call will look like this:

from deid.dicom import get_identifiers
ids = get_identifiers(dicom_files)

You’ll get back a dictionary(indexed by the file name) for each dicom file. Within each entry, the value is another dictionary with an expanded string of the tag. For example:

{'(0008, 0005)': (0008, 0005) Specific Character Set              CS: 'ISO_IR 100'  [SpecificCharacterSet],
 '(0008, 0016)': (0008, 0016) SOP Class UID                       UI: Secondary Capture Image Storage  [SOPClassUID],
 '(0008, 0018)': (0008, 0018) SOP Instance UID                    UI:  [SOPInstanceUID],
 '(0008, 0020)': (0008, 0020) Study Date                          DA: '20131210'  [StudyDate],
 '(0008, 0030)': (0008, 0030) Study Time                          TM: '191929'  [StudyTime],
 '(0008, 0050)': (0008, 0050) Accession Number                    SH: ''  [AccessionNumber],
 '(0008, 0064)': (0008, 0064) Conversion Type                     CS: 'WSD'  [ConversionType],
 '(0008, 0080)': (0008, 0080) Institution Name                    LO: 'STANFORD'  [InstitutionName],
 '(0008, 0090)': (0008, 0090) Referring Physician's Name          PN: 'Dr. solitary heart'  [ReferringPhysicianName],
 '(0008, 1060)': (0008, 1060) Name of Physician(s) Reading Study  PN: 'Dr. lively wind'  [NameOfPhysiciansReadingStudy],
 '(0008, 1070)': (0008, 1070) Operators' Name                     PN: 'curly darkness'  [OperatorsName],
 '(0010, 0010)': (0010, 0010) Patient's Name                      PN: 'falling disk'  [PatientName],
 '(0010, 0020)': (0010, 0020) Patient ID                          LO: 'cookie-47'  [PatientID],
 '(0010, 0030)': (0010, 0030) Patient's Birth Date                DA: ''  [PatientBirthDate],
 '(0010, 0040)': (0010, 0040) Patient's Sex                       CS: 'M'  [PatientSex],
 '(0020, 000d)': (0020, 000d) Study Instance UID                  UI:  [StudyInstanceUID],
 '(0020, 000e)': (0020, 000e) Series Instance UID                 UI:  [SeriesInstanceUID],
 '(0020, 0010)': (0020, 0010) Study ID                            SH: ''  [StudyID],
 '(0020, 0011)': (0020, 0011) Series Number                       IS: ''  [SeriesNumber],
 '(0020, 0013)': (0020, 0013) Instance Number                     IS: ''  [InstanceNumber],
 '(0020, 0020)': (0020, 0020) Patient Orientation                 CS: ''  [PatientOrientation],
 '(0020, 4000)': (0020, 4000) Image Comments                      LT: 'This is a cookie tumor dataset for testing dicom tools.'  [ImageComments],
 '(0028, 0002)': (0028, 0002) Samples per Pixel                   US: 3  [SamplesPerPixel],
 '(0028, 0004)': (0028, 0004) Photometric Interpretation          CS: 'YBR_FULL_422'  [PhotometricInterpretation],
 '(0028, 0006)': (0028, 0006) Planar Configuration                US: 0  [PlanarConfiguration],
 '(0028, 0010)': (0028, 0010) Rows                                US: 1536  [Rows],
 '(0028, 0011)': (0028, 0011) Columns                             US: 2048  [Columns],
 '(0028, 0100)': (0028, 0100) Bits Allocated                      US: 8  [BitsAllocated],
 '(0028, 0101)': (0028, 0101) Bits Stored                         US: 8  [BitsStored],
 '(0028, 0102)': (0028, 0102) High Bit                            US: 7  [HighBit],
 '(0028, 0103)': (0028, 0103) Pixel Representation                US: 0  [PixelRepresentation],
 '(0028, 2110)': (0028, 2110) Lossy Image Compression             CS: '01'  [LossyImageCompression],
 '(0028, 2114)': (0028, 2114) Lossy Image Compression Method      CS: 'ISO_10918_1'  [LossyImageCompressionMethod],
 '(7fe0, 0010)': (7fe0, 0010) Pixel Data                          OB: Array of 652494 bytes  [PixelData]}

If there is a nested tag, you’ll see it with the format (7fe0, 0010)__(0080, 0012). If there is a nested sequence, you’ll see the index provided in that same format. For example, (7fe0, 0010)__0__(0080, 0012) counts as the first element of a sequence, and (7fe0, 0010)__1__(0080, 0012) the second. We start counting at 0, we aren’t barbarians!


The content of each field is a DicomField, which carries with it the dicom tag (string), name (string), and the actual element for further parsing. For example:

field = ids[dicom_files[0]]['(0010, 0010)']

(0010, 0010) Patient's Name                      PN: 'falling disk'

'(0010, 0010)'

The field.element is what you would get if you indexed the dicom Dataset at dicom.get(“PatientName”). The name refers to the keyword (which, if there is nesting, will include that. For example, a Sequence with header value AdditionalData and item Modality will be returned as AdditionalData_Modality, and this name string is used to help with filters. The uid would also include the index of the sequence, since we use it to index into the Dataset.

Next Steps

The get_identifiers function is an easy way to quickly extract (in bulk) multiple identifiers for inspection, across a lot of files. You might be writing or developing a recipe, and need easy access to all these fields. What should you do next? At this point, you have a few options:

Recipe Interaction

If you want to write a recipe to perform a bunch of custom actions on your dicom files, you should read about how to work with recipes.

Clean Pixels

It’s likely that the pixels in the images have burned in annotations, and we can use the header data to flag these images. Thus, before you replace identifiers, you probably want to do this. We have a DicomCleaner class that can flag images for PHI based on matching some header filter criteria, and you can read about that here.

Update Identifiers

Once you are finished with any customization of the recipe, updating identifiers, and/or potentially flagging and quarantining images that have PHI, you should be ready to replace (PUT) with new fields based on the deid recipe.