Releases: ImagingDataCommons/highdicom
Releases · ImagingDataCommons/highdicom
v0.23.1
Patch release with a few minor bug fixes
Bug Fixes
- Update docs to reflect python 3.10 dependency by @CPBridge in #308
- Allow searching SRs using LongCodeValue and URNCodeValue. Fix for #309 by @rhaxton in #310
- Fix for BOT construction with pydicom 3, by @CPBridge in #314
- Fix SR if
SpacingBetweenSlices
is not set, by @Fedalto in #315
New Contributors
Full Changelog: v0.23.0...v0.23.1
v0.23.0
Dependencies
- Highdicom now depends upon pydicom > 3.0.1. #301
- Highdicom now requires python > 3.10. This was necessitated by a similar move from pydicom. #301
- Remove references to
numpy.float_
to allow working with numpy>2
Tooling/Repo
- We have adopted the contributor covenant. #271
- Various style improvements #286 #287 #289 #290 #291 #292
- We have moved to
pyproject.toml
metadata. #293 - Improve automated checks to enforce repo review rules #296
Features
- Further checks on graphic data for SRs #276
- Additional checks for microscopy bulk annotation coordinate types #281
- Further improvements in segmentation creation efficiency #285
- Allow creation of pyramid segmentations with floating point arrays, or with multiple segments #297
- Add options allowing to infer the subject context from an image #298
- Use pydicom 3 features to enable additional transfer syntaxes in compression.
- Add methods to get a list of images used as evidence within an SR #303
- Add a
further_source_images
option to the segmentation constructor #304
Fixes
- Minor fixes for microscopy bulk annotation graphic data #278
- Remove the JP2 wrapper from JPEG 2000 encoding
Docs
- Added a gitflow section to the developer guide. #272
v0.22.0
Probably left this one far too long...
New Features
- New features for parsing existing Microscopy Bulk Annotations:
annread
function andannotation_coordinate_type
property (#230) - Multiprocessing for frame encoding in segmentation construction (#245)
- A major set of improvements for working with tiled segmentations including ability to pass in total pixel matrices to the segmentation constructor, the ability to create and read TILED_FULL segmentations, and the ability to construct segmentation total pixel matrices from tiled images (#248)
- New function to create multiresolution segmentation pyramids (#253)
Bug fixes
- Allow duplicate entries in the ReferencedSeriesSequence of a segmentation image (#229)
- Remove plane orientation from the shared functional groups in the case of segs using the slide coordinate system (#236), a DICOM compliance issue
- Exclude incompatible pydicom 2.4.0 in
setup.py
(#238) - Fixes to various value representations (#239)
- Fix return type of
highdicom.seg.DimensionIndexSequence.get_plane_positions_of_image
(#240) - Correctly account for chrominance subsampling of natively encoded
YBR_FULL_422
images in theImageFileReader
(#242) - Work around pillow 10.0.0 breaking changes (#244)
- Specimen description and preparation fixes within microscopy related content items (#246)
- A number of style improvements (#257 #258 #259 #261 #262 #263 #264 #265 #268)
Performance improvements
- Significant improvements to segmentation creation efficiency (#227)
Documentation and tests
- Add
codespell
tool to check for spelling errors in the docs (#237) - Fix documentation links (#250)
- Fix the readthedoc config (#256)
- Fix to an incorrectly written frame encoding test (#270)
- Use latest version of github actions (#266)
New contributors
Thanks to @yarikoptic @thomas-albrecht @elitalien and @DimitriPapadopoulos for their first contributions to highdicom!
v0.21.1
Bug fixes
- Correctly deal with
LongCodeValue
andURNCodeValue
inCodedConcept.from_dataset()
(#226) - Remove an unnecessary table join when fetching segmentation pixel (#224)
- Fix
ImageFileReader
's handling ofDicomFileLike
objects, meaning that you can now read frames from a open file handle or apydicom.filebase.DicomBytesIO
object (an in-memory buffer) (#223).
New contributors
- Thanks to @RobinFrcd for his first contribution to the library (#223)!
v0.21.0
New features
- The implementation of methods for constructing segmentation pixels arrays from a
highdicom.seg.Segmentation
object (highdicom.seg.Segmentation.get_pixels_by_source_instance()
,highdicom.seg.Segmentation.get_pixels_by_source_frame()
, andhighdicom.seg.Segmentation.get_pixels_by_dimension_index_values()
) have been considerably refactored with a general focus on improving the usability for large segmentation objects (#208). These changes are compatible with existing code except that in some cases the methods may return numpy arrays with a smaller unsigned integer data type than they previously did. User code should see significant speed-ups without any changes. The new versions have several improvements:- Improvements in computational efficiency due to a redesign of the way the frame look-up table is stored under the hood. Now an in-memory sqlite database is used through the Python standard library
sqlite3
module. This allows for considerably faster and more flexible querying. - Significant improvements in memory efficiency for the case where
combine_segments=True
. Previously the memory usage scaled as O(n) in the number of segments, now it is constant (O(1)). - When combining segments, the methods now automatically determine and return an appropriate unsigned integer datatype to return the smallest array that can represent all segments. This has been observed to reduce both the memory usage and improve speed (largely due to the reducing the need to allocate memory for unnecessarily large numpy arrays)
- There is a new parameter,
dtype
, that allows the user to choose the data type of the output array (overriding the automatically determined default). - There is a further new boolean parameter
skip_overlap_checks
, which allows the user to specify that the check for overlapping segments in the case wherecombine_segments=True
is skipped. This makes a significant difference to runtime. If this is done and two segments do overlap, the segment with the highest output segmentation number will be placed into the output array preferentially. The default behaviour matches the previous behaviour in that checks for overlapping segments are performed, and an error is raised if any two segments overlaps. - The user guide is updated to the preferred way of accessing pixel data using the above methods.
- Improvements in computational efficiency due to a redesign of the way the frame look-up table is stored under the hood. Now an in-memory sqlite database is used through the Python standard library
- There is now an optional parameter in
from_dataset()
methods calledcopy
. By default, this parameter is True, meaning that a full deepcopy of the original dataset is made before conversion to the highdicom class, which matches the previous behaviour. This is the "safest" option that prevents potentially unwanted behaviour downstream if the user tries to re-use the original dataset. However if the user chooses to set this parameter toFalse
, then the deepcopy is skipped and the original dataset is updated in place. This can give a very significant speed-up when the segmentation object are large. Additionally this is used in thesegread
andsrread
functions to give a significant speed up as it is never necessary to deepcopy the temporary object read from file (#207). - Added a new function
highdicom.sr.srread()
, similar to the existinghighdicom.seg.segread()
, to read a dataset representing a supported Structured Report SOP Class from a file and convert it to the appropriate highdicom class automatically (#215). - Users may now pass a single-element Sequence to the
content
parameter of the__init__
methods of Structured Report SOP classes, as alternative to passing apydicom.Dataset
. This is more intuitive for users that have constructed ahighdicom.sr.MeasuremenrtReport
class and wish to use it as the content of a new Structured Report (#216).
Enhancements
- The library's repository was moved to the ImagingDataCommons organization on GitHub, and all URLs were updated (#212).
- The library's Github Actions now run the tests using Python 3.11 in addition to older versions (#217) to ensure that highdicom supports the latest Python version.
Bug fixes
- A minor tweak to the routine for segmentation construction that avoids creating a copy of large portions of the input array just to find the unique values (#221).
- A bug, resulting in the
ReferencedImageSequence
of ahighdicom.ann.MicroscopyBulkSimpleAnnotations
always being empty, was resolved (#220). - A mistake in the docstrings of the
PixelToReferenceTransformer
,ReferenceToPixelTransformer
, andImageToReferenceTransformer
classes was fixed (#209). - A bug that resulted in GSPS creation failing when the referenced images have multiple values for WindowWidth, WindowCenter and/or WindowCenterWidthExplanation was fixed (#211).
v0.20.0
v0.20.0
New features
- Move
pylibjpeg
and related dependencies (pylibjpeg-libjpeg
andpylibjpeg-openjpeg
) from requirements to optional requirements. This means that the default installation is compatible with highdicom's MIT license. There are now some transfer syntaxes that are not supported with the default installation. Users can install highdicom with the optional dependencies by specifyinghighdicom[libjpeg]
as their installation target. Update github test runners to support both the cases where the optional requirements are installed and are not installed (#201).
Enhancements
- Add citation file to allow citing the package (#204).
Bug fixes
- Use a deepcopy for
CodedConcept.from_dataset()
to avoid issues with optional attributes of the sequence getting lost (#205). - When the omit_empty_frames option is used for a Segmentation and an empty segmentation mask is passed (i.e. a mask with all zeros), the constructor will issue a UserWarning and ignore the omit_empty_frames option (#181).