dicube 0.3.0

Medical Image Storage Library with DICOM compatibility

DiCube: Medical Image Storage Library

DiCube is a Python library for efficient storage and processing of 3D medical images with complete DICOM metadata preservation. It provides a high-compression, single-file format that combines DICOM compatibility with modern compression techniques.

Install

pip install dicube

Overview

DiCube was extracted from the larger DICOMCube project to focus specifically on medical image storage. It works alongside:

• spacetransformer: For 3D spatial transformations and coordinate systems
• medmask: For medical image segmentation mask processing

Why DiCube?

Although the DICOM standard is indispensable for clinical interoperability, it was never designed for today’s high-volume, AI-driven workflows. In practice it exposes four chronic pain-points:

1. Fragmented storage → slow I/O A single CT/MR study can contain hundreds of small .dcm files. Traversing the file system and parsing each header one-by-one wastes precious milliseconds that quickly add up when training deep-learning models/ building realtime application.
2. Redundant metadata Every slice repeats identical patient/study/series tags, inflating storage size and network traffic with no benefit.
3. Too many transfer syntaxes Vendors ship proprietary or rarely-used encodings; no open-source decoder reliably supports all of them, so pipelines break on edge-cases.

DiCube mitigates these issues by:

• Single-file design All slices, metadata and spatial information are consolidated into one .dcbs file, eliminating filesystem overhead.
• Deduplicated metadata A compact JSON schema separates shared and per-slice tags, then compresses them with Zstandard.
• Conservative codec policy Only codecs that pass extensive performance and stability tests are adopted. Currently .dcbs uses HTJ2K for fast, lossless compression. Archive (.dcba) and lossy (.dcbl) variants are planned but not yet released. This package is a self-contained lib, users do not need to install codecs independently.
• Round-trip safety Every DiCube file can always be converted back to standard DICOM, preserving full clinical fidelity.

In typical benchmarks DiCube cuts CT series load time from ~150 ms (PyDICOM) to <40 ms and shrinks storage by up to 3×, all while remaining 100 % DICOM-compatible.

Architecture

Core Modules

dicube/
├── core/                 # Core data structures
│   ├── image.py         # DicomCubeImage (main interface)
|   ├── io.py            # DicomCubeImageIO (from and to many file formats)
│   └── pixel_header.py  # PixelDataHeader (image metadata)
├── storage/             # File storage formats
│   ├── dcb_file.py      # DCB file format implementations
│   └── pixel_utils.py   # Pixel processing utilities
├── dicom/               # DICOM functionality
│   ├── dicom_meta.py    # DicomMeta (metadata container)
│   ├── dicom_status.py  # DICOM consistency checking
│   ├── dicom_tags.py    # DICOM tag definitions
│   ├── dicom_io.py      # DICOM file I/O
│   └── merge_utils.py   # Metadata merging utilities
├── codecs/              # Compression codecs
│   └── jph/            # HTJ2K codec (dcbs format)
└── exceptions.py        # Custom exceptions

File Formats

DiCube defines three file format specifications for different use cases:

.dcbs (Speed format) - Currently Implemented

• Magic: DCMCUBES
• Target: I/O speed suitable for deep learning training while high compression ratio.
• Codec: High Throughput JPEG 2000 (HTJ2K)
• Use case: High-speed encoding/decoding for processing pipelines
• Features: Optimized for throughput, lossless compression

.dcba (Archive format) - Placeholder

• Magic: DCMCUBEA
• Target: 20% better compression ratio than dcbs
• Use case: Long-term storage and archiving
• Status: Awaiting suitable codec that meets compression targets

.dcbl (Lossy format) - Placeholder

• Magic: DCMCUBEL
• Target: 60%+ compression ratio with imperceptible quality loss
• Use case: High-compression scenarios where minor quality trade-offs are acceptable
• Status: Awaiting suitable codec that meets quality/compression targets

Codec Selection Philosophy: We take a conservative approach to codec adoption, requiring extensive testing and clear performance benefits before implementation. This avoids the complexity issues seen in DICOM's numerous format variations.

Key Classes and Interfaces

DicomCubeImage

Main interface for medical image handling:

import dicube

# Create from DICOM directory
image = dicube.load_from_dicom_folder('path/to/dicom/')

# Create from NIfTI file
image = dicube.load_from_nifti('image.nii.gz')

# Save to compressed format (currently only dcbs is implemented)
dicube.save(image, 'output.dcbs', file_type='s')  # HTJ2K (Speed format)

# Load from file
loaded_image = dicube.load('output.dcbs')

# Export back to DICOM
dicube.save_to_dicom_folder(image, 'output_dicom/')

# Get pixel data
pixel_data = image.get_fdata()  # Returns float array
raw_data = image.raw_image       # Returns original dtype

DicomMeta

DICOM metadata container with efficient shared/non-shared value handling:

import dicube
from dicube import get_dicom_status
from dicube.dicom import CommonTags

# Load complete image (includes all metadata)
image = dicube.load_from_dicom_folder('dicom_folder/')
meta = image.dicom_meta

# Or load only metadata from a DiCube file
meta = dicube.load_meta('scan.dcbs')

# Access shared values (same across all slices)
patient_name = meta.get_shared_value(CommonTags.PatientName)  # Returns single value

# Access non-shared values (different per slice)
positions = meta.get_values(CommonTags.ImagePositionPatient)  # Returns list

# Check if a field is shared across all slices
is_uniform_spacing = meta.is_shared(CommonTags.PixelSpacing)

# Check DICOM status
status = get_dicom_status(meta)

Integration with spacetransformer

DiCube uses spacetransformer.Space for 3D coordinate system handling:

from spacetransformer import Space, warp_image

# DicomCubeImage automatically creates Space from DICOM
image = dicube.load_from_dicom_folder('dicom/')
space = image.space  # spacetransformer.Space object

# Apply spatial transformations
space2 = space.apply_flip(axis=2)
space2 = space2.apply_rotate(axis=0, angle=90, unit='degree')

# Update image with new space
image2 = warp_image(image, space, space2)

DICOM Status Checking

DiCube provides comprehensive DICOM consistency checking:

from dicube import DicomStatus, get_dicom_status

status = get_dicom_status(meta)

# Possible status values:
# DicomStatus.CONSISTENT - All checks pass
# DicomStatus.MISSING_SERIES_UID - No series UID
# DicomStatus.DUPLICATE_INSTANCE_NUMBERS - Non-unique instance numbers
# DicomStatus.NON_UNIFORM_SPACING - Inconsistent pixel spacing
# DicomStatus.GAP_LOCATION - Missing slices in Z direction
# ... and more

Compression Codecs

HTJ2K (jph/)

• Status: Currently implemented for .dcbs format
• Files: _encode.py, _decode.py, pybind11 bindings
• Functions: imencode_jph(), imdecode_jph()
• Build: Uses pybind11 for C++ bindings to OpenJPH library
• Performance: Optimized for high-speed encoding/decoding

Best Practices

For Medical Images

1. Always preserve DICOM metadata when possible
2. Currently use .dcbs format for all storage needs (fast HTJ2K)
3. Check DICOM status before processing: get_dicom_status(meta)
4. Monitor for updates as .dcba and .dcbl formats become available

For Integration

1. Use spacetransformer for all spatial operations
2. Use medmask for segmentation mask processing
3. Convert coordinates between voxel and world space using Space transforms
4. Validate file format compatibility with dicube.load()

Performance Tips

1. Use num_threads parameter for parallel compression
2. For large datasets, process in chunks to manage memory
3. Check DicomStatus before processing to avoid corrupted data
4. Use HTJ2K's high-speed capabilities for processing pipelines

Error Handling

from dicube.exceptions import (
    DicomCubeError,
    InvalidCubeFileError, 
    CodecError,
    MetaDataError,
    DataConsistencyError
)

try:
    image = dicube.load('corrupted.dcbs')
except InvalidCubeFileError:
    print("Not a valid DiCube file")
except CodecError:
    print("Compression/decompression failed")
except MetaDataError:
    print("Missing or invalid metadata")

Dependencies

Required

• numpy: Array operations
• pydicom: DICOM file handling
• spacetransformer: Spatial transformations
• zstandard: Metadata compression

Optional (for full functionality)

• OpenJPH library: For .dcbs format implementation
• nibabel: For NIfTI file support