easy-medical-images 1.0.1

The easy way to manipulate medical images.

EasyMedicalImages

The easy way to manipulate medical images

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This is a library that focuses on taking CT scans and transforming them into arrays in python; giving the user easy ways to manipulate them as well. This can easily be used for downstream usage of CT images within a Python script.

Installation

Requires Python 3.12 or newer.

pip install easy-medical-images

The high-level batch loader (HighLevel) is a compiled C++ extension. The published wheels include it, so a normal pip install is all you need.

Expected data layout

Each case lives in its own folder containing an image and a segmentation. By default these are named image.nii.gz and tumor_segmentation_v2.nii.gz, but you can override both names (see image_path and seg_path below).

parent_folder/
├── case_0001/
│   ├── image.nii.gz
│   └── tumor_segmentation_v2.nii.gz
├── case_0002/
│   ├── image.nii.gz
│   └── tumor_segmentation_v2.nii.gz
└── ...

Remeber to put the proper root for the input directory path! For example, if your files live on a cloud.

CTImagesLibrary operates on a single case folder. HighLevel operates on a parent folder and loads every case folder inside it.

How it fits together

Generally, this library assumes the user wants a batch of something rather than a specific image. This is reflected in how you first load in your specific instance, and then can add modifiers how you wish.

There are two main sub libraries:

1. HighLevel: point it at a parent directory and it loads every case in parallel (after the method 'run_on_directory' is called on the instance). This will return a list of (image, seg) array pairs. Use this to pull a whole dataset into memory quickly.
2. CTImagesLibrary: load one case and get the full manipulation toolkit (slicing, cropping, reorientation, display). Use this when you want to work with an individual scan. See below for examples.

Quick start: load a whole directory

import EasyMedicalImages as EMI

path_to_folder_of_ct_scans = input("Input path to CT scans: ")
instance = EMI.HighLevel(path_to_folder=path_to_folder_of_ct_scans)

img_seg_pairs_mega_list = instance.run_on_directory()  # list of (image, seg) array pairs
'''
img_seg_pairs_mega_list=[(img1, seg1),(img2, seg2)...]
'''

# Each entry is one case
img, seg = img_seg_pairs_mega_list[0]
print(f"{len(img_seg_pairs_mega_list)} cases loaded")
print(f"First image shape: {img.shape}, seg shape: {seg.shape}")

Every entry in img_seg_pairs_mega_list is a tuple of two NumPy float32 arrays: the image and its segmentation. Both are already resampled to isotropic spacing and reoriented to RAS, so they are ready to use.

To access all of the images or all of the segs while keeping order, one can simply do:

all_images=[images for images,_ in img_seg_pairs_mega_list]
all_segmentations=[segmentations for _, segmentations in img_seg_pairs_mega_list]

# Both of the below will print the entire array
print(f"Images: {all_images}")
print(f"Segmentation Masks: {all_segmentations}")

# This just shows the shape, easier to comprehend.
print([(img.shape, seg.shape) for img, seg in img_seg_pairs_mega_list])

Working with a single case

from EasyMedicalImages import CTImagesLibrary
 
case = CTImagesLibrary(path_to_folder="path/to/one/case")
 
# The voxels are automatically processed upon calling of the instance, unlike the batch version.
print(case.mutated_img_data.shape)
print(case.mutated_seg_data.shape)
 
# Display three orthogonal slices (slice numbers are 1-based, ordered X, Y, Z)
case.display_slice(slice_number=(175, 279, 57), img_or_seg="img")
 
# Get those same slices back as arrays
slice_x, slice_y, slice_z = case.return_slice(slice_number=(175, 279, 57), img_or_seg="img")
 
# Crop the whole volume down to the segmented region (modifies the mutated arrays in place)
case.apply_seg_crop_to_image(img_or_seg="both")
print(case.mutated_img_data.shape)  # smaller now

As stated earlier, building CTImagesLibrary automatically: changes the working directory into path_to_folder, loads the image and segmentation, and builds isotropic, RAS-reoriented copies. After construction, both the raw and processed arrays are available as attributes via your_instancename.raw_img_data, or for seg your_instancename.raw_seg_data.

Further detail

This section goes one level deeper on each of the two sublibraries: how you construct it, and what you can call on it once you have an instance. Both are reached through the top-level package (import EasyMedicalImages as EMI, then EMI.HighLevel or EMI.CTImagesLibrary).

HighLevel

The batch sublibrary. Point it at a parent folder, call run_on_directory, and get every case back as (image, seg) array pairs. This is the one to reach for when you want a whole dataset in memory at once.

Construct it (default parameters):

HighLevel(
    path_to_folder,
    image_path="image.nii.gz",
    seg_path="tumor_segmentation_v2.nii.gz",
    verbose=False,
    thread_deadline_time=66.0,
    verbose_workers=True,
)

• path_to_folder: parent folder whose immediate subfolders are individual cases.
• image_path, seg_path: file names to look for inside each case folder.
• verbose: passed through to each worker for extra logging.
• thread_deadline_time: per-case wall-clock budget in seconds. A worker that runs longer is killed and that case is skipped. This is to prevent a thread, which is just stalling or trying to load a not image, from stoping the whole process.
• verbose_workers: print per-worker progress (which case each worker picked up, load confirmations, and the final count).

Methods:

• run_on_directory(directory_path="") Loads every case in the directory and returns a list of (image, seg) tuples, one per case, in the same order as the folders on disk. Both elements are NumPy float32 arrays, already resampled to isotropic spacing and reoriented to RAS. If directory_path is given it overrides path_to_folder for that one call. Cases that fail to load or exceed thread_deadline_time are dropped, so the returned list can be shorter than the number of folders in theory (so don't set this too low).

Loading runs in parallel: one worker subprocess per case, across roughly 80 percent of the available hardware threads. On Linux it respects the CPU affinity set by schedulers such as SLURM, so it behaves correctly on a cluster node. Note that this method returns images and segmentations only; it does not return affines.

• how_many_threads_available() Returns the number of hardware threads detected (the figure the worker pool is sized from). Honors SLURM cpuset limits on Linux and falls back sensibly when the OS reports nothing.

CTImagesLibrary

The single-case sublibrary. Construct it on one case folder and the data is processed and waiting on the instance; from there you slice, crop, reorient, and display. This is the toolkit HighLevel runs under the hood for each case, so the arrays you get out of either path match.

Construct it (default parameters):

CTImagesLibrary(
    path_to_folder,
    image_path="image.nii.gz",
    seg_path="tumor_segmentation_v2.nii.gz",
    verbose=True,
    return_affines=False,
)

The image and segmentation are loaded and processed during construction, so the attributes below are ready as soon as the instance exists.

Attributes on the instance:

• raw_img_data, raw_seg_data: the original voxel arrays as loaded.
• raw_img_affine, raw_seg_affine: the original 4x4 affine matrices.
• mutated_img_data, mutated_seg_data: isotropic, RAS-reoriented copies. These are usually what you want for machine learning.
• mutated_img_affine, mutated_seg_affine: affines describing the processed grid.

methods:

• display_slice(slice_number=(), cmap="viridis", view_axes=("X", "Y", "Z"), load_mutated=True, crop=False, pad=5, img_or_seg="img") Shows one slice per requested axis side by side with matplotlib. slice_number is a 1-based (X, Y, Z) tuple and must contain exactly three values. Set load_mutated=False to view the original (non-isotropic) volume, crop=True to trim each slice to its own non-background region (with pad voxels of margin), and img_or_seg="seg" to view the segmentation instead.

• return_slice(slice_number=(), view_axes=("X", "Y", "Z"), load_mutated=True, one_list=False, crop=False, pad=5, img_or_seg="img") Returns slices as arrays instead of displaying them. With the default three axes it returns (slice_x, slice_y, slice_z); set one_list=True to get them as a single array of three instead (convenient for batching). Request a single axis (for example view_axes=("Z",)) to get just that one slice back. The arrays match exactly what display_slice shows.

• apply_seg_crop_to_image(pad=5, return_data=False, img_or_seg="both") Crops the full 3D volume in place to the bounding box of the segmentation, keeping pad voxels of margin. This overwrites mutated_img_data and mutated_seg_data (and their affines) with the cropped versions. Use img_or_seg to crop only "img", only "seg", or "both" (the default). If the segmentation is empty, nothing is cropped. Set return_data=True to also return the cropped arrays.

• apply_seg_crop_to_slice(slice_number=(), one_list=False, pad=5, img_or_seg="img") The 2D version: returns the X, Y, and Z slices cropped to the segmentation's footprint in each plane. Unlike the 3D version, this runs on the image or the segmentation, not both at once (img_or_seg is "img" by default). slice_number is the same 1-based (X, Y, Z) tuple.

Notes on orientation

The processed arrays (mutated_img_data and friends) are in proper RAS orientation. The slices produced by display_slice and return_slice add a 90-degree flip on the Z axis so they read naturally in matplotlib (similar to how ITK displays them). In short: use the instance arrays when you need correct anatomical orientation, and use return_slice when you want something that looks right on screen.

PyPI link: PyPI