Changelog

0.3.5 - 2026-02-19

Configuration Loading Behavior: load_configs(), from_yaml(), from_json(), and from_dict() now default to loading only the provided configurations, without including standard predefined configs. Pass load_standard=True to include standard configs alongside loaded ones. RadiomicsPipeline() default constructor behavior is unchanged.

Memory Exhaustion Issue: Resolved a critical issue where resampled background voxels (value 0) were included in the ROI if they fell within the resegment range. The pipeline now explicitly applies the source_mask to the intensity_mask after resampling when source_mode="auto" or an explicit source mask is used. This prevents memory explosions for small ROIs in large volumes with sentinel backgrounds.
Pipeline Configuration Serialization: Fixed a bug where source_mode and sentinel_value were lost during serialization (to_dict/to_yaml).
Sentinel Detection: Fixed detection logic to correctly handle auto-generated full masks.

Documentation: Updated Data Loading and Pipeline user guides to clarify the usage of source_mode="auto" vs "full_image" and its impact on memory and correctness.

Sentinel Value Implementation: Implemented proper handling of sentinel values in the pipeline to assure that they do not influence the feature extraction.
Complete overhaul of User Guide: Rewrote the user guide to improve clarity and organization.
Benchmark Methodology Updates: Refined timing methods for benchmarking with optimized measurement techniques, resulting in up to 40% faster execution for PyRadiomics compared to previous implementations. Therefore speed improvements of pictologics are now more modest.

Feature Deduplication System: Intelligent optimization for multi-configuration pipelines:
- Automatically detects when configurations share preprocessing steps but differ only in discretization
- Computes discretization-independent features (morphology, intensity) once and reuses across configurations
- deduplication_stats property provides reuse/compute statistics after each run
- Hash-based signature comparison using SHA256 for exact parameter matching
- Versioned rules system (DeduplicationRules) for reproducibility

Deduplication enabled by default: RadiomicsPipeline(deduplicate=True) is now the default behavior
Documentation updated:
- Case examples simplified to reflect default deduplication behavior
- Benchmark page clarifies methodology (raw timing without caching) and notes additional speedups with deduplication

Pipeline Configuration Serialization: Full YAML/JSON export/import for RadiomicsPipeline configurations:
- save_configs() / load_configs(): File-based configuration persistence
- to_yaml() / from_yaml(): String-based YAML serialization
- to_json() / from_json(): String-based JSON serialization
- to_dict() / from_dict(): Dictionary conversion for programmatic use
Configuration Management Methods:
- add_config(): Register custom configurations
- get_config(): Retrieve configuration by name (deep copy)
- remove_config(): Delete configurations
- list_configs(): List all registered configuration names
- merge_configs(): Combine configurations from multiple pipelines
Template System: YAML-based configuration templates in pictologics/templates/:
- Standard configurations now loaded from standard_configs.yaml
- Template loading API: list_template_files(), load_template_file(), get_standard_templates(), get_all_templates(), get_template_metadata()
Schema Versioning: Configuration files include schema_version for forward compatibility and automatic migration
Configuration Validation: Opt-in validation via validate=True parameter logs warnings for unknown steps/parameters
Documentation: New "Predefined Configurations" user guide page with comprehensive examples including end-to-end multi-site study workflow

Standard configurations (standard_fbn_*, standard_fbs_*) now loaded from YAML templates instead of hardcoded dictionaries
Updated pipeline.md documentation with condensed configuration section and cross-references

IBSI 2 Convolutional Filters: Complete filter module (pictologics/filters/) with:
- Mean filter (3D)
- Laplacian of Gaussian (LoG)
- Laws texture energy filters (3D rotation-invariant)
- Gabor filters (2D per-slice)
- Wavelet decomposition (Haar, Daubechies, Coiflet, Symlet families)
- Simoncelli steerable pyramid
IBSI 2 Phase 1 Compliance: Filter response map validation against digital phantoms
IBSI 2 Phase 2 Compliance: Feature extraction from filtered images validated
IBSI 2 Phase 3 Compliance: Multi-modality reproducibility validation across 51 patients × 3 modalities compared to 9 team submissions
Filter Pipeline Integration: New filter step in RadiomicsPipeline for seamless filtered feature extraction
Mask Binarization Pipeline Step: New binarize_mask preprocessing step with configurable threshold, mask_values (int/list/range tuple), and apply_to targeting.

IBSI 1 Compliance (Morphology): Achieved passing values for Compactness 2 (BQWJ) and Asphericity (25C7) in Configs C/D/E and texture matrices in config D by binarizing masks before resampling.

DICOM Database Utility: DicomDatabase class for parsing complex DICOM folder hierarchies with Patient → Study → Series → Instance traversal, multi-phase detection, and DataFrame/JSON/CSV exports
DICOM SEG Loader: load_seg() for loading DICOM Segmentation objects with multi-segment handling, geometry alignment, and seamless auto-detection in load_image()
DICOM SR Parser: SRDocument class for parsing Structured Reports with measurement extraction, CSV/JSON export, and batch processing via SRDocument.from_folders()
DICOM Multi-Phase Support: load_image() now supports multi-phase DICOM series with dataset_index, plus get_dicom_phases() for phase discovery
Visualization Utility: visualize_slices() for interactive viewing and save_slices() for batch export with window/level normalization and colormap options
Cropped Image Repositioning: load_image() and load_and_merge_images() support repositioning cropped masks into reference volume coordinate space
Intensity Rescaling: apply_rescale parameter in load_image and related functions to toggle DICOM rescale slope/intercept application (default: True)
Sentinel Value Handling: Documentation and examples for handling sentinel values (e.g. -2048 in Siemens DICOMs) using the resegment preprocessing step
Dependencies: Added highdicom, matplotlib, pillow; updated pandas>=2.0.0

Morphology Speedup: Implemented bounding box cropping for morphology features (mesh/moments), significantly accelerating extraction for sparse ROIs in large volumes
Texture Speedup: Added slice-level skipping to texture calculation to ignore empty z-slices, vastly improving performance for disjoint ROIs (e.g. multiple tumors)

DICOM file loading improvements: proper Z-spacing, 3D SEG handling, direction matrix extraction

DicomDatabase uses shared split_dicom_phases() for consistent multi-phase detection
Comprehensive documentation updates for all new utilities

Initial commit