cardiac-shared 0.9.1

Shared utilities for cardiac imaging analysis - batch management, preprocessing pipelines, hardware detection, IO, data sources, vertebra detection, tissue classification, dataset registry, thickness detection, paired dataset management

Cardiac Shared

Shared utilities for cardiac imaging analysis projects.

Version: 0.9.1 | PyPI: https://pypi.org/project/cardiac-shared/

Installation

# Install from PyPI (recommended)
pip install cardiac-shared

# Install with optional dependencies
pip install cardiac-shared[all]      # All optional deps
pip install cardiac-shared[dicom]    # DICOM support
pip install cardiac-shared[nifti]    # NIfTI support
pip install cardiac-shared[gpu]      # GPU/PyTorch support

Modules

IO Module

Function	Description
read_dicom_series(path)	Read DICOM series from directory
get_dicom_metadata(ds)	Extract metadata from DICOM dataset
load_nifti(path)	Load NIfTI file with metadata
save_nifti(volume, path)	Save numpy array as NIfTI
extract_zip(path)	Context manager for ZIP extraction
find_dicom_root(path)	Find DICOM directory in extracted ZIP
AsyncNiftiPreloader	Background NIfTI preloading (v0.5.1)
preload_nifti_batch()	Batch preloading convenience function (v0.5.1)

Hardware Module

Function	Description
detect_hardware()	Detect complete hardware info (GPU/CPU/RAM)
HardwareInfo	Dataclass with GPU, CPU, RAM, environment info
print_hardware_summary(hw)	Print formatted hardware summary
get_optimal_config(hw)	Get optimal inference configuration
CPUOptimizer	CPU optimization for hospital deployments
get_recommended_gpu_stabilization_time()	Dynamic GPU wait time (v0.5.1)
get_gpu_performance_tier()	GPU tier classification (v0.5.1)

Environment Module

Function	Description
detect_runtime()	Detect runtime environment
RuntimeEnvironment	Dataclass with environment info
detect_colab()	Check if running in Google Colab
detect_wsl()	Check if running in WSL

Parallel Module (v0.3.0)

Class/Function	Description
ParallelProcessor	Unified parallel processing framework
parallel_map()	Quick parallel map without checkpoint
parallel_map_with_checkpoint()	Parallel map with resume support
ProcessingResult	Result dataclass for each processed item
Checkpoint	Checkpoint data for resume capability

Progress Module (v0.3.0)

Class/Function	Description
ProgressTracker	Multi-level progress visualization
create_tracker()	Create and start a progress tracker
ProgressLevel	Progress tracking for a single level

Cache Module (v0.3.0)

Class	Description
CacheManager	Multi-level caching with resume capability

Batch Module (v0.3.0)

Class	Description
BatchProcessor	Generic batch processing framework
BatchConfig	Batch processing configuration

Config Module (v0.3.0)

Class/Function	Description
ConfigManager	YAML/JSON configuration management
load_config()	Load configuration with defaults

Data Module (v0.4.0)

Class/Function	Description
IntermediateResultsRegistry	Cross-project data discovery and sharing
RegistryEntry	Dataclass for registry entries
get_registry()	Get singleton registry instance

Data Sources Module (v0.5.0)

Class/Function	Description
DataSourceManager	Multi-source data management with YAML config
DataSource	Configuration for a single data source
DataSourceStatus	Status check result for a data source
get_source()	Get data source from default manager
list_sources()	List all configured data sources

Vertebra Module (v0.5.0)

Class/Function	Description
VertebraDetector	Detect and analyze vertebrae from TotalSegmentator output
VertebraInfo	Vertebra metadata (center slice, volume, etc.)
VertebraROI	Region of interest around a vertebra
parse_vertebrae()	Parse vertebra names from labels directory
sort_vertebrae()	Sort vertebrae cranial to caudal

Tissue Module (v0.5.0)

Class/Function	Description
TissueClassifier	Tissue-specific HU filtering (Alberta Protocol 2024)
TissueMetrics	Metrics for a tissue type (area, HU, quality)
FilterStats	Statistics from HU filtering
filter_tissue()	Filter tissue mask by HU range
get_tissue_hu_range()	Get HU range for tissue type
TISSUE_HU_RANGES	Standard HU ranges for all tissue types

Batch Management Module (v0.6.0)

Class/Function	Description
BatchManager	Create and manage batch manifests
BatchManifest	Track patients, status, and consumers
PatientEntry	Individual patient processing record
ConsumerRecord	Module that consumed a batch
create_nifti_batch()	Create NIfTI batch manifest
load_batch()	Load existing batch manifest

Batch Discovery Module (v0.6.4)

Class/Function	Description
BatchDiscovery	Discover and select from multiple processing batches
BatchInfo	Information about a discovered batch
PatientBatchRecord	Patient record within a batch
discover_batches()	Convenience function to discover batches
list_batches()	List all discovered batch IDs
find_patient()	Find a patient across all batches
select_latest_batch()	Select the latest batch matching criteria
get_patient_coverage()	Check coverage of patients across batches

Dataset Registry Module (v0.8.0 - Configuration-Driven)

Class/Function	Description
DatasetRegistry	Registry framework for dataset definitions
DatasetRegistry.from_yaml(path)	Load datasets from YAML configuration
Dataset	Dataset definition with patient count and metadata
DatasetStatus	Processing status (PLANNED, COMPLETED, VALIDATED)
DatasetCategory	Category (INTERNAL, EXTERNAL, FUTURE)
load_registry_from_yaml(path)	Load global registry from YAML
get_dataset_registry()	Get global registry instance (empty by default)

Key Features:

• Configuration-driven: Dataset definitions loaded from YAML, not hardcoded
• Privacy-safe: Internal/private data stays in local config files, not in PyPI
• Flexible updates: Change data counts without releasing new package versions

Usage:

from cardiac_shared.data import DatasetRegistry, load_registry_from_yaml

# Load from your project's config file
registry = DatasetRegistry.from_yaml("config/datasets_registry.yaml")

# Or use the global registry
load_registry_from_yaml("config/datasets_registry.yaml")
chd = get_dataset("internal.chd")
print(f"CHD patients: {chd.patient_count}")

Preprocessing Module (v0.6.0)

Class/Function	Description
DicomConverter	Unified DICOM to NIfTI conversion
ConversionResult	Conversion result details
convert_dicom_to_nifti()	Simple conversion function
SharedPreprocessingPipeline	Multi-module preprocessing
PreprocessingConfig	Pipeline configuration
PreprocessingResult	Preprocessing result details
create_pipeline()	Create configured pipeline

Usage Examples

Hardware Detection

from cardiac_shared import detect_hardware, detect_runtime

hw = detect_hardware()
print(f"GPU: {hw.gpu.device_name if hw.gpu.available else 'None'}")
print(f"CPU Cores: {hw.cpu.physical_cores}")
print(f"RAM: {hw.ram.total_gb:.1f} GB")

env = detect_runtime()
print(f"Runtime: {env.runtime_type}")  # wsl, linux, windows, colab

Parallel Processing with Checkpoint

from cardiac_shared.parallel import ParallelProcessor

def process_patient(patient_id):
    # Your processing logic
    return {"id": patient_id, "status": "done"}

processor = ParallelProcessor(
    max_workers=4,
    checkpoint_file="results/checkpoint.json"
)

results = processor.map_with_checkpoint(
    process_patient,
    patient_list,
    desc="Processing patients"
)

processor.print_summary(results)

Progress Tracking

from cardiac_shared.progress import ProgressTracker

tracker = ProgressTracker()
tracker.start_overall("Processing Pipeline", total=100)

for i, item in enumerate(items):
    tracker.start_step(f"Step {i+1}", total=3)

    tracker.update_substep("Loading data")
    # ... load data
    tracker.update_step_progress()

    tracker.update_substep("Processing")
    # ... process
    tracker.update_step_progress()

    tracker.complete_step()
    tracker.update_overall(i + 1)

tracker.finish()

Cache Management

from cardiac_shared.cache import CacheManager

cache = CacheManager("results/cache.json")

for patient_id in patient_list:
    if cache.is_completed(patient_id):
        continue  # Skip already processed

    result = process_patient(patient_id)
    cache.mark_completed(patient_id, result)

Configuration Management

from cardiac_shared.config import ConfigManager

config = ConfigManager("config/settings.yaml")
db_host = config.get("database.host", default="localhost")
config.set("processing.batch_size", 32)
config.save()

Intermediate Results Registry (v0.4.0)

from cardiac_shared.data import get_registry

# Get singleton registry instance
registry = get_registry()

# Check if TotalSegmentator results are available
if registry.exists('segmentation.totalsegmentator_organs.cohort_v2'):
    organs_path = registry.get_path('segmentation.totalsegmentator_organs.cohort_v2')
    heart_mask = organs_path / patient_id / 'heart.nii.gz'

# Get metadata
meta = registry.get_metadata('body_composition.stage1_labels.cohort_v1')
print(f"Patient count: {meta.get('patient_count')}")

# List available results
available = registry.list_available('segmentation')

# Get usage suggestion for a project
suggestion = registry.suggest_input('pericardial_fat', 'heart_masks', 'cohort')

Data Source Management (v0.5.0)

from cardiac_shared.data_sources import DataSourceManager

# Load from project config
manager = DataSourceManager('/path/to/data_sources.yaml')

# Or use project auto-discovery
manager = DataSourceManager.from_project('my-project')

# Get data source
source = manager.get_source('default')
print(f"Input: {source.input_dir}")
print(f"Files: {source.file_count()}")

# Get input files
for file in source.get_files(limit=10):
    process(file)

# Check all sources status
manager.print_status()

Vertebra Detection (v0.5.0)

from cardiac_shared.vertebra import VertebraDetector, parse_vertebrae
import numpy as np

# Simple parsing
vertebrae = parse_vertebrae('/path/to/labels')
print(f"Found: {vertebrae}")  # ['T10', 'T11', 'T12', 'L1']

# Full analysis
detector = VertebraDetector()
vertebrae_info = detector.find_vertebrae('/path/to/labels')

for v in vertebrae_info:
    print(f"{v.name}: center slice {v.center_slice}")

# Get center slice from mask
mask = np.load('vertebrae_T12.npy')
center = detector.get_center_slice(mask)

Tissue Classification (v0.5.0)

from cardiac_shared.tissue import TissueClassifier, filter_tissue, TISSUE_HU_RANGES
import numpy as np

# Check HU ranges
print(TISSUE_HU_RANGES['skeletal_muscle']['range'])  # (-29, 150)

# Filter tissue by HU
filtered_mask, stats = filter_tissue(ct_array, mask, 'skeletal_muscle')
print(f"Retention: {stats.retention_pct:.1f}%")

# Full metrics calculation
classifier = TissueClassifier()
metrics = classifier.calculate_metrics(
    ct_array, mask, 'skeletal_muscle',
    spacing=(1.0, 0.5, 0.5),
    slice_idx=50  # Single slice
)
print(f"Area: {metrics.area_cm2:.1f} cm^2")
print(f"Mean HU: {metrics.mean_hu:.1f}")
print(f"Quality: {metrics.quality_grade}")

Batch Management (v0.6.0)

from cardiac_shared.data import BatchManager, create_nifti_batch

# Create batch manager
manager = BatchManager(output_dir="/data/nifti")

# Create batch for NIfTI conversion
manifest = manager.create_batch(
    dataset_id="study_cohort_v1",
    source_path="/data/dicom/cohort",
    provider="Hospital A",
)

# Check for existing conversion (deduplication)
existing = manager.find_existing_nifti("P001234", "study_cohort_v1")
if not existing:
    # Process and register
    manager.register_patient(
        dataset_id="study_cohort_v1",
        patient_id="P001234",
        status="success",
        output_file="P001234.nii.gz",
        dimensions=[512, 512, 256]
    )

# Track consumer modules
manager.register_consumer("study_cohort_v1", "pericardial_fat", "analysis_run_001")

Batch Discovery (v0.6.4)

from cardiac_shared.data import BatchDiscovery

# Discover all TotalSegmentator batches
discovery = BatchDiscovery("/data/totalsegmentator")

# List all available batches
for batch_id in discovery.list_batches(prefix="organs_cohort"):
    info = discovery.get_batch_info(batch_id)
    print(f"{batch_id}: {info['total_patients']} patients, created {info['created_at']}")

# Select the latest batch
batch = discovery.select_latest_batch(prefix="organs_cohort", require_success_count=100)
print(f"Selected: {batch.batch_id}")

# Find a patient across all batches
records = discovery.find_patient("P001234")
if records:
    latest = records[0]  # Most recent
    heart_mask = latest.patient_path / "heart.nii.gz"
    print(f"Found in {latest.batch_id}: {heart_mask}")

# Check coverage for a patient list
coverage = discovery.get_patient_coverage(patient_ids, batch_prefix="organs_cohort")
print(f"Coverage: {coverage['coverage_rate']:.1f}% ({coverage['covered']}/{coverage['total']})")

Preprocessing Pipeline (v0.6.0)

from cardiac_shared.preprocessing import SharedPreprocessingPipeline, create_pipeline

# Create pipeline
pipeline = create_pipeline(
    nifti_root="/data/nifti",
    segmentation_root="/data/totalsegmentator",
    totalsegmentator_fast=True,
)

# Ensure NIfTI exists (converts if needed)
result = pipeline.ensure_nifti("P001234", "study_cohort_v1", dicom_path)
print(f"NIfTI: {result.output_path}")

# Ensure TotalSegmentator results exist
result = pipeline.ensure_totalsegmentator("P001234", "study_cohort_v1")
print(f"Segmentation: {result.output_path}")

# Get masks for specific analysis module
masks = pipeline.get_module_masks("P001234", "study_cohort_v1", "pericardial_fat")
heart_mask = masks["heart"]

# Validate masks for an analysis module
valid, missing = pipeline.validate_for_module("P001234", "study_cohort_v1", "perivascular_fat")
if not valid:
    print(f"Missing masks: {missing}")

Use Cases

This package supports various cardiac imaging analysis workflows:

• Vertebral body composition analysis
• Pericardial fat analysis
• Coronary artery calcium scoring
• Multi-organ CT segmentation pipelines

Changelog

See CHANGELOG.md for full version history.

v0.9.1 (2026-01-31)

• Remove all Chinese characters from source code and documentation
• Remove hardcoded internal project names from public API
• Generalize DataSourceManager.from_project() to accept arbitrary project names
• Rename MODULE_REQUIREMENTS keys to descriptive analysis type names
• Clean all code examples to use generic placeholders
• Update README version and documentation

v0.9.0 (2026-01-21)

• Added preprocessing/thickness.py (CT slice thickness detection)
• Added data/paired_dataset.py (paired thin/thick dataset management)

v0.8.1 (2026-01-04)

• Stable configuration-driven DatasetRegistry

See CHANGELOG.md for full version history.

License

MIT License - see LICENSE for details.