Loading Images: Quickstart

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PathML provides support for loading a wide array of imaging modalities and file formats under a standardized syntax. In this vignette, we highlight code snippets for loading a range of image types ranging from brightfield H&E and IHC to highly multiplexed immunofluorescence and spatial expression and proteomics, from small images to gigapixel scale:

Imaging modality

File format

Source

Image dimensions (X, Y, Z, C, T)

Brightfield H&E

Aperio SVS

OpenSlide example data

(32914, 46000, 1, 3, 1)

Brightfield H&E

Generic tiled TIFF

OpenSlide example data

(32914, 46000, 1, 3, 1)

Brightfield IHC

Hamamatsu NDPI

OpenSlide example data

(73728, 126976, 1, 3, 1)

Brightfield H&E

Hamamatsu VMS

OpenSlide example data

(76288, 102400, 1, 3, 1)

Brightfield H&E

Leica SCN

OpenSlide example data

(153470, 53130, 1, 3, 1)

Fluorescence

MIRAX

OpenSlide example data

(170960, 76324, 1, 3, 1)

Brightfield IHC

Olympus VSI

OpenSlide example data

(6753, 13196, 1, 3, 1)

Brightfield H&E

Trestle TIFF

OpenSlide example data

(25408, 61504, 1, 3, 1)

Brightfield H&E

Ventana BIF

OpenSlide example data

(93951, 105813, 1, 3, 1)

Fluorescence

Zeiss ZVI

OpenSlide example data

(1388, 1040, 13, 3, 1)

Brightfield H&E

DICOM

Orthanc example data

(30462, 78000, 1, 3, 1)

Fluorescence (CODEX spatial proteomics)

TIFF

Schurch et al., Cell 2020

(1920, 1440, 17, 4, 23)

Fluorescence (time-series + volumetric)

OME-TIFF

`OME-TIFF example data <https://downlo ads.openmicroscopy.o rg/images/OME-TIFF/2 016-06/tubhiswt-4D/> `__

(512, 512, 10, 2, 43)

Fluorescence (MERFISH spatial gene expression)

TIF

Zhuang et al., 2020

(2048, 2048, 7, 1, 40)

Fluorescence (Visium 10x spatial gene expression)

TIFF

10x Genomics

(25088, 26624, 1, 1, 4)

All images used in these examples are publicly available for download at the links listed above.

Note that across the wide diversity of modalities and file formats, the syntax for loading images is consistent (see examples below).

[1]:
# import utilities for loading images
from pathml.core import HESlide, CODEXSlide, VectraSlide, SlideData, types

Aperio SVS

[2]:
my_aperio_image = HESlide("./data/CMU-1.svs")

Generic tiled TIFF

[3]:
my_generic_tiff_image = HESlide("./data/CMU-1.tiff", backend = "bioformats")

Hamamatsu NDPI

The labels field can be used to store slide-level metadata. For example, in this case we store the target gene, which is Ki-67:

[4]:
my_ndpi_image = SlideData("./data/OS-2.ndpi",
                          labels = {"taget" : "Ki-67"},
                          slide_type = types.IHC)

Hamamatsu VMS

[5]:
my_vms_image = HESlide("./data/CMU-1/CMU-1-40x - 2010-01-12 13.24.05.vms", backend = "openslide")

Leica SCN

[6]:
my_leica_image = HESlide("./data/Leica-1.scn")

MIRAX

[7]:
my_mirax_image = SlideData("./data/Mirax2-Fluorescence-1/Mirax2-Fluorescence-1.mrxs",
                           slide_type = types.IF)

Olympus VSI

Again, we use the labels field to store slide-level metadata such as the name of the target gene.

[8]:
my_olympus_vsi = SlideData("./data/OS-3/OS-3.vsi",
                           labels = {"taget" : "PTEN"},
                           slide_type = types.IHC)

Trestle TIFF

[9]:
my_trestle_tiff = SlideData("./data/CMU-2/CMU-2.tif")

Ventana BIF

[10]:
my_ventana_bif = SlideData("./data/OS-1.bif")

Zeiss ZVI

Again, we use the labels field to store slide-level metadata such as the name of the target gene.

[11]:
my_zeiss_zvi = SlideData("./data/Zeiss-1-Stacked.zvi",
                         labels = {"target" : "HER-2"},
                         slide_type = types.IF)

DICOM

[12]:
my_dicom = HESlide("./data/orthanc_example.dcm")

Volumetric + time-series OME-TIFF

[13]:
my_volumetric_timeseries_image = SlideData(
    "./data/tubhiswt-4D/tubhiswt_C1_TP42.ome.tif",
    labels = {"organism" : "C elegans"},
    volumetric = True,
    time_series = True,
    backend = "bioformats"
)

CODEX spatial proteomics

The labels field can be used to store whatever slide-level metadata the user wants; here we specify the tissue type

[14]:
my_codex_image = CODEXSlide('../../data/reg031_X01_Y01.tif',
                            labels = {"tissue type" : "CRC"});

MERFISH spatial gene expression

[15]:
my_merfish_image = SlideData("./data/aligned_images0.tif", backend = "bioformats")

Visium 10x spatial gene expression

Here we load an image with accompanying expression data in AnnData format.

[16]:
# load the counts matrix of spatial genomics information
import scanpy as sc
adata = sc.read_10x_h5("./data/Visium_FFPE_Mouse_Brain_IF_raw_feature_bc_matrix.h5")

# load the image, with accompanying counts matrix metadata
my_visium_image = SlideData("./data/Visium_FFPE_Mouse_Brain_IF_image.tif",
                            counts=adata,
                            backend = "bioformats")
Variable names are not unique. To make them unique, call `.var_names_make_unique`.
Variable names are not unique. To make them unique, call `.var_names_make_unique`.