isicarchive

ISIC Archive API


License
MIT
Install
pip install isicarchive==0.4.0

Documentation

isic-archive (ISIC Archive access python module)

The ISIC Archive is an online repository and archive published and maintained by the International Skin Imaging Collaboration. Next to the human-readable and browsable website, it also provides a publicly available API, which offers several functions (called endpoints) for interacting with the data programmatically.

The present python package is an attempt at bundling the more frequently used functionality into a single module, thus reducing the need to re-write certain code for a diverse set of projects.

First steps

To start with, please import the IsicApi class from the isicarchive module and create an instance of the class:

from isicarchive import IsicApi
api = IsicApi()

Data availability

All general features are available without logging into the API. However, since many datasets (and with them their images) as well as studies are not marked as being "publicly available", the number of items returned by many functions (endpoints) differ based on whether you have successfully authenticated with the API. If you do not plan to register a username, you can skip the next section, and either set the username parameter to None or skip it altogether in the call to IsicApi.

Logging into the ISIC Archive

For instance, some annotations created by study participants, or retrieving certain images that are not marked for public access requires that you are logged into the archive/API. This can be achieved by instantiating the IsicApi object with a valid username (and password):

# set username
username = 'address@provider.com'

# create API object
api = IsicApi(username)

# or, if you can securely store a password as well
api = IsicApi(username, password)

Please do not enter the password in clear text into your source code. If you provide only the username, the password will be requested from either the console or, if used in a Jupyter notebook, below the active cell using the getpass library.

Local cache folder

Since a lot of the data that can be retrieved from the archive (API) is relatively static--that is, it will not change between uses of the API--you can keep a locally cached copy, which will speed up processing of data on the next call you use the same image or annotation, for instance. To do so, please add the cache_folder parameter to the call, like so:

# For Linux/Mac
cache_folder = '/some/local/folder'
# For Windows
cache_folder = 'C:\\Users\\username\\some\\local\\folder' # use double \\ !

# Create object
api = IsicApi(cache_folder=cache_folder)
# or
api = IsicApi(username, cache_folder=cache_folder)
# or
api = IsicApi(username, password, cache_folder=cache_folder)

Relatively large and complex data (annotations, images, etc.) will have a stored local copy, which means that they can be retrieved later from the cache, instead of having to request them again from the web-based API.

Within the cache folder the IsicApi object will, on first use, create 16 subfolders, named 0 through 9, and a through f (the 16 hexadecimal digits), to avoid downloading too many files into a single folder, which would slow down the operation later on. For each file, the sub-folder is determined by the last hexadecimal digit of the unique object ID (explained below).

Images are stored with a filename pattern of image_[objectId]_[name].ext whereas objectId is the unique ID for this image within the archive, name is the filename (typically ISIC_xxxxxxx), and .ext is the extension as provided by the Content-Type header of the downloaded image.

Superpixel images (also explained below) are stored with the filename pattern of spimg_[objectId].png using the associated image's object ID! In addition, a derived superpixel index array is stored with a filename pattern of spidx_[objectID].npz (using numpy.savez).

Caching information about all images

Since the archive contains several thousand images, it can often be helpful to be able to search for specific images. To do so locally, you can download the details about all images available in the archive (if you're) calling the IsicApi object with the cache_folder parameter) like so:

# Populate image cache
api.cache_images()

# display information about image ISIC_000000 (by its ID) from the cache
image_info = api.image_cache[api.images['ISIC_0000000']]
print(image_info)

When called for the first time, building the cache may take several minutes. Once the information is downloaded, however, only a single call will be made to the web-based API to confirm that, indeed, no new images are available. For this to work, however, it is important that you do not use the same cache folder for sessions where you are either logged in (authenticated) versus not! The cache file itself will be stored in the file named [cache_folder]/0/imcache_000000000000000000000000.json.gz.

Finally, feature annotations associated with a specific study can be downloaded in bulk and cached using this syntax:

# Load annotation markup feature superpixel arrays
study.load_annotations()

The resulting file will be stored in stann_[objectId].json.gz, and not for each annotation object separately, so that loading will be much faster.

Some more details on the web-based API

Any interaction with the web-based API is performed by the IsicApi object through the HTTPS protocol, using the requests package methods. As part of the requests made, the endpoint (function and type of element being interacted with) is specified, and one or several parameters can be set, which are appended to the URL. For instance, retrieving information about one specific image would be achieved by accessing the following URL:

https://isic-archive.com/api/v1/image/5436e3abbae478396759f0cf

Object IDs and element representation

This last portion of the URL that appears after the image/ part is called the (object) id, and is a system-wide unique value that identifies each element to ensure that one interacts only with the intended target. The output of the URL above is (slightly truncated for brevity):

{
  "_id": "5436e3abbae478396759f0cf",
  "_modelType": "image",
  "created": "2014-10-09T19:36:11.989000+00:00",
  "creator": {
    "_id": "5450e996bae47865794e4d0d",
    "name": "User 6VSN"
  },
  "dataset": {
    "_accessLevel": 0,
    "_id": "5a2ecc5e1165975c945942a2",
    "description": "Moles and melanomas.",
    "license": "CC-0",
    "name": "UDA-1",
    "updated": "2014-11-10T02:39:56.492000+00:00"
  },
  "meta": {
    "acquisition": {
      "image_type": "dermoscopic",
      "pixelsX": 1022,
      "pixelsY": 767
    },
    "clinical": {
      "age_approx": 55,
      "anatom_site_general": "anterior torso",
      "benign_malignant": "benign",
      "diagnosis": "nevus",
      "diagnosis_confirm_type": null,
      "melanocytic": true,
      "sex": "female"
    }
  },
  "name": "ISIC_0000000",
  "updated": "2015-02-23T02:48:17.495000+00:00"
}

Pretty much all elements available through the API are returned in the form of their JSON representation (notation) as text. Lists of elements are returned as arrays. The exception are binary blobs (such as image data, superpixel image data, and mask images).

Within the ISIC archive (and thus for the API), the following elements are recognized:

  • images (having both a JSON and several associated binary blob elements)
  • segmentations (also having a JSON and a binary mask image component)
  • datasets (collection of images)
  • studies (selection of images from multiple datasets, together with questions and features to be annotated by users)
  • annotations (responses to questions and image-based per-feature annotation as a selection of "superpixels")
  • users (information about each registered user)
  • tasks (information about tasks assigned to the logged in user)

Of these, currently accessible via the IsicApi object are image, study, dataset, and annotation.

Image superpixels

As part of the image processing capabilities of the ISIC Archive itself, each image that is uploaded will be automatically compartmentalized into about 1,000 patches of roughly equal size. E.g. for an image with a 4-by-3 aspect ratio, there would be roughly 36 times 27 superpixels. The superpixel information is stored in a specifically RGB-encoded image, such that for each superpixel the patch has a (for the computer uniquely represented) RGB color code:

ISIC_0000000 image superpixels

The IsicApi.image.Image class contains functions to decode and map this image first into an index array, and then into a mapping array:

from isicarchive import IsicApi

# load superpixel image for first image
api = IsicApi()
image = api.image('ISIC_0000000')
image.load_superpixels()
superpixel_index_image = image.superpixels['idx']
image.map_superpixels()
superpixel_mapping = image.superpixels['map']

This mapping array can be used to rapidly access (e.g. extract or paint over) the pixels in the actual color image of a skin lesion:

# paint over superpixel with index 472 in an image with red (RGB=(255,0,0))
image = api.image('ISIC_0000000')
image.load_imagedata()
image.load_superpixels()
image.map_superpixels()
image_data = image.data
image_shape = image_data.shape
image_data.shape = (image_shape[0] * image_shape[1], -1)
map = image.superpixels['map']
superpixel_index = 472
pixel_count = map[superpixel_index, -1]
superpixel_pixels = map[superpixel_index, 0:pixel_count]
image_data[superpixel_pixels, 0] = 255
image_data[superpixel_pixels, 1] = 0
image_data[superpixel_pixels, 2] = 0
image_data.shape = image_shape

# show image
import matplotlib.pyplot as plt
plt.imshow(image_data)
plt.show()

Retrieving information about a study

The syntax below will make a call to the web-based API, and retrieve the information about the study named in study_name. If the study is not found, an exception is raised! Other than the web-based API (which does not support names), you do not have to look up the object ID manually first. The returned value, study is an object of type isicarchive.Study, which provides some additional methods.

# Retrieve study object
study = api.study('ISBI 2016: 100 Lesion Classification')

# Download all accessible images and superpixel images for this study
study.cache_imagedata()

# Print study features
study.load_annotations()
print(study.features)

In addition to the information regularly provided by the ISIC Archive API, the IsicApi object's implementation can also attempt to mass-download information about all annotations.

Retrieving information about a dataset

dataset = api.dataset(dataset_name)

Similarly to a study, this will create an object of type isicarchive.Dataset, which allows additional methods to be called.

In addition to the information regularly provided by the ISIC Archive API, the IsicApi object's implementation will also attempt to already download information about the access list, metadata, and images up for review.

Retrieving images

# Load the first image of the loaded study
image = api.image(study.images[0])

This will, initially, only load the information about the image. If you would like to make the binary data available, please use the following methods:

# Load image data
image.load_imagedata()

# Load superpixel image data
image.load_superpixels()

# Parse superpixels into a mapping-to-pixel-indices array
image.map_superpixels()

The mapping of an image takes a second or so, but storing the map in a different format would be relatively wasteful, and so this seems preferable.

Selecting images

The IsicApi object allows to select images based on the contents of any subfield in the image details (JSON) representation:

# Make initial selection
selection = api.select_images([
    ['meta.acquisition.pixelsX', '>=', 2048],
    ['meta.acquisition.image_type', '==', 'dermoscopic'],
    ['meta.clinical.diagnosis', '!=', 'nevus'],
])

# refine selection (you can inspect the results after each step)
selection = api.select_images(['dataset._accessLevel', '==', 0], sub_select=True)
selection = api.select_images(['notes.tags', 'ni', 'ISBI 2017: Training'], sub_select=True)
selection = api.select_images(['meta.unstructured.biopsy done', '==', 'Y'], sub_select=True)
selection = api.select_images(['meta.clinical.melanocytic', 'is', True], sub_select=True)

The selection will both be returned, and also stored in the api.image_selection field. So, in a Jupyter notebook, please assign the result to a variable if it is the last statement in a cell and you wish not to print the output!