i am a beginner in LIDC dataset.could u please tell me how to specify path in config file.pylidcrc.I tried using the tutorial,but i got the following error.
vol = scan.to_volume()
2 print(vol.shape)
3
4 print("%.2f, %.2f" % (vol.mean(), vol.std()))

~/anaconda3/lib/python3.6/site-packages/pylidc/Scan.py in to_volume(self, verbose)
637 Return the scan as a 3D numpy array volume.
638 """
--> 639 images = self.load_all_dicom_images(verbose=verbose)
640
641 volume = np.zeros((512,512,len(images)))

~/anaconda3/lib/python3.6/site-packages/pylidc/Scan.py in load_all_dicom_images(self, verbose)
286 if verbose: print("Loading dicom files ... This may take a moment.")
287
--> 288 path = self.get_path_to_dicom_files()
289 fnames = [fname for fname in os.listdir(path)
290 if fname.endswith('.dcm')]

~/anaconda3/lib/python3.6/site-packages/pylidc/Scan.py in get_path_to_dicom_files(self)
213 "specified the path option in the configuration "
214 "file {}?")
--> 215 raise RuntimeError(msg.format(_get_config_file()))
216
217 base = os.path.join(dicompath, self.patient_id)

RuntimeError: Could not establish path to dicom files. Have you specified the path option in the configuration file /home/user/.pylidcrc?

Hey,
when running

scan = pl.query(pl.Scan).filter(pl.Scan.patient_id == 'LIDC-IDRI-0005').first()
scan.to_volume()

I'm getting

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.5/dist-packages/pylidc/Scan.py", line 453, in to_volume
    images.append( dicom.read_file(f) )
  File "/usr/local/lib/python3.5/dist-packages/dicom/filereader.py", line 614, in read_file
    force=force)
  File "/usr/local/lib/python3.5/dist-packages/dicom/filereader.py", line 515, in read_partial
    preamble = read_preamble(fileobj, force)
  File "/usr/local/lib/python3.5/dist-packages/dicom/filereader.py", line 477, in read_preamble
    preamble = fp.read(0x80)
  File "/usr/lib/python3.5/codecs.py", line 321, in decode
    (result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xc4 in position 140: invalid continuation byte

for all patient DICOM files I tried so far.
Do you see what might be the problem?

I just wanted to update that I've successfully used pylidc on Window 8 with python 3.5.

The analogous location for .pylidcrc is C:\Users\[User]\.pylidcrc. There is a minor issue with windows not allowing to creating a file with an empty filename (starting with a dot). The simple workaround is to add another dot in the end of the filename: .pylidcrc..

Received the error below while running the example code

AttributeError Traceback (most recent call last)
in ()
17
18 ann = q.first()
---> 19 print("%.2f, %.2f, %.2f" % (ann.diameter, ann.surface_area, ann.volume))
20 # => 17.98, 1221.40, 1033.70

AttributeError: 'Annotation' object has no attribute 'diameter'

This issue is sort of a follow up for the problem that was mentioned in pull-request #5 (similiar to issue #2)

In short:
Some of the .dcm files are missing (not only 0000.dcm), which results in an error.

I checked with the LIDC online access to make sure that the problem isn't the result of failed downloads:

• Some cases are like patient 1009, where all files are present, but the first index is 3. Or patient 0777, where all 187 files are present but start at number 5.
• In patient 0048, the missing file (298) is the last one, but the total number of files is again consistant with the online dataset (297).

At the time, it seemed to me that some of the missing files were in the middle. However, I don't see any such cases in my log.

Code:
scan= pl.query(pl.Scan).filter(pl.Scan.series_instance_uid == '1.3.6.1.4.1.14519.5.2.1.6279.6001.250863365157630276148828903732' ).first() scan.annotations[0].uniform_cubic_resample(side_length = 100)

Error:

Loading dicom files ... This may take a moment.

FileNotFoundError Traceback (most recent call last)
in ()
----> 1 scan.annotations[0].uniform_cubic_resample(side_length = 100)

E:\Anaconda2\envs\LIDC\lib\site-packages\pylidc\Annotation.py in uniform_cubic_r
esample(self, side_length, resample_vol, irp_pts, return_irp_pts, verbose)
768
769 # Load the images. Get the z positions.
--> 770 images = self.scan.load_all_dicom_images(verbose=verbose)
771 img_zs = [float(img.ImagePositionPatient[-1]) for img in images]
772 img_zs = np.unique(img_zs)
E:\Anaconda2\envs\LIDC\lib\site-packages\pylidc\Scan.py in load_all_dicom_images
(self, verbose)
315 images = []
316 for dicom_file_name in sorted_fnames:
--> 317 with open(os.path.join(path, dicom_file_name), 'rb') as f:
318 images.append( dicom.read_file(f) )
319 return images

FileNotFoundError: [Errno 2] No such file or directory: 'E:/Library/Datasets/DOI
\LIDC-IDRI-0048\1.3.6.1.4.1.14519.5.2.1.6279.6001.2081777976054741511065201243
06\1.3.6.1.4.1.14519.5.2.1.6279.6001.250863365157630276148828903732\000298.dcm
'

Full list of missing files that I encountered:

• LIDC-IDRI-0048\1.3.6.1.4.1.14519.5.2.1.6279.6001.208177797605474151106520124306\1.3.6.1.4.1.14519.5.2.1.6279.6001.250863365157630276148828903732\000298.dcm
• LIDC-IDRI-1011\1.3.6.1.4.1.14519.5.2.1.6279.6001.287560874054243719452635194040\1.3.6.1.4.1.14519.5.2.1.6279.6001.272123398257168239653655006815\000001.dcm
• LIDC-IDRI-1011\1.3.6.1.4.1.14519.5.2.1.6279.6001.287560874054243719452635194040\1.3.6.1.4.1.14519.5.2.1.6279.6001.272123398257168239653655006815\000003.dcm
• LIDC-IDRI-1011\1.3.6.1.4.1.14519.5.2.1.6279.6001.287560874054243719452635194040\1.3.6.1.4.1.14519.5.2.1.6279.6001.272123398257168239653655006815\000002.dcm
• LIDC-IDRI-1010\1.3.6.1.4.1.14519.5.2.1.6279.6001.145373944605191222309393681361\1.3.6.1.4.1.14519.5.2.1.6279.6001.550599855064600241623943717588\000001.dcm
• LIDC-IDRI-1010\1.3.6.1.4.1.14519.5.2.1.6279.6001.145373944605191222309393681361\1.3.6.1.4.1.14519.5.2.1.6279.6001.550599855064600241623943717588\000002.dcm
• LIDC-IDRI-1009\1.3.6.1.4.1.14519.5.2.1.6279.6001.849069697860879761549990488101\1.3.6.1.4.1.14519.5.2.1.6279.6001.855232435861303786204450738044\000001.dcm
• LIDC-IDRI-1009\1.3.6.1.4.1.14519.5.2.1.6279.6001.849069697860879761549990488101\1.3.6.1.4.1.14519.5.2.1.6279.6001.855232435861303786204450738044\000002.dcm
• LIDC-IDRI-0777\1.3.6.1.4.1.14519.5.2.1.6279.6001.226719444846209417020566423366\1.3.6.1.4.1.14519.5.2.1.6279.6001.192256506776434538421891524301\000004.dcm
• LIDC-IDRI-0777\1.3.6.1.4.1.14519.5.2.1.6279.6001.226719444846209417020566423366\1.3.6.1.4.1.14519.5.2.1.6279.6001.192256506776434538421891524301\000001.dcm
• LIDC-IDRI-0777\1.3.6.1.4.1.14519.5.2.1.6279.6001.226719444846209417020566423366\1.3.6.1.4.1.14519.5.2.1.6279.6001.192256506776434538421891524301\000005.dcm
• LIDC-IDRI-0777\1.3.6.1.4.1.14519.5.2.1.6279.6001.226719444846209417020566423366\1.3.6.1.4.1.14519.5.2.1.6279.6001.192256506776434538421891524301\000002.dcm
• LIDC-IDRI-0777\1.3.6.1.4.1.14519.5.2.1.6279.6001.226719444846209417020566423366\1.3.6.1.4.1.14519.5.2.1.6279.6001.192256506776434538421891524301\000003.dcm
• LIDC-IDRI-0127\1.3.6.1.4.1.14519.5.2.1.6279.6001.195975724868929317649402600442\1.3.6.1.4.1.14519.5.2.1.6279.6001.229343399861261429237689489892\000001.dcm

running qu.all() shows all the id's for 97 patients, but the LIDC dataset has over 1000 patients. Is there an easy way to incorporate the others?

In 'matplotlib version == 3.6.2'

matplotlib.cm.cmap_d is not working in visualize_in_3d of Annotation.py code.

Just change the matplotlib.cm.cmap_d --> matplotlib.cm.cmaps_listed, and can use this.

This question might be stupid. is it possible to access NBIA? If then how can I do it?

https://www.cancerimagingarchive.net/nbia-search/?CollectionCriteria=LIDC-IDRI

OSError: Couldn't find DICOM files for Scan(id=12,patient_id=LIDC-IDRI-0001).

pylidc.conf is:
[dicom]
path =C:\Users\Pollob\ct img\LIDC-IDRI
warn = True

actually where is the problem? anyone please help me.i am not able to fix****

Thanks for your very useful library!
Would it make sense to automatically install sqlalchemy as well when pylidc is beeing installed?
Update: when installing dependencies using pip install -r requirements.txt, one gets the error ModuleNotFoundError: No module named 'sqlalchemy' which isn't even resolved when putting sqlalchemy ahead of pylidc.

Hello, I'm trying to use pylidc for another database of images that I have. Therefore, I've edited populate.py to populate the database with my image data, and edited the .conf file. However after running populate.py successfully, it doesn't seem to have any effect as pl.query(pl.Scan) still pulls up the old list of patients ("LIDC-IDRI-xxxx"). Do I need to do anything else besides editing populate.py?

The package is sorely lacking in unit testing

In scitkit image versions 14 and above, the function marching_cubes does not exist. Either marching_cubes_classic or marching_cubes_lewiner must be called explicitly.

Hi, there a GUI for visualizing the scans and some meta information by using scan.visualize(annotation_groups=nods)

However, is it possible to extract the information about the slice number of annotation e.g. nodule 1, near slice 25
how can I extract this information using the pulidc functions?

Thank you

I was adding a set of DICOM files to test on, as well as an XML file for Pylidc to read from. As such, I put the files in my LIDC-IDRI directory, and the path of the files are 'D:\LIDC-IDRI\LIDC-IDRI-1012\2.25.83166935061457396929107941819169230974\2.25.270306605818510402659631454860337221712'
Therefore, StudyInstanceUID = 2.25.83166935061457396929107941819169230974
and SeriesInstanceUID = 2.25.270306605818510402659631454860337221712

However, when my test program runs the following:
scan = pl.query(pl.Scan).filter(pl.Scan.patient_id == 'LIDC-IDRI-1012').first() print(scan.get_path_to_dicom_files())
it returns an error that it can't find the DICOM files.
File "C:\Users\Reader\Documents\scripts\dev\pylidc\pylidc\Scan.py", line 256, in get_path_to_dicom_files raise IOError("Couldn't find DICOM files for %s."%self) OSError: Couldn't find DICOM files for Scan(id=643,patient_id=LIDC-IDRI-1012)

Moreover, printing scan.annotations reveals that the annotations are also wrong/misread.
Not sure how Pylidc gets the wrong UIDs.

Following the instructions and example here: https://pylidc.github.io/tuts/scan.html#scan-visualization, the result is a static image, not interactive visualization.

See demo of the actual behavior here: https://www.screencast.com/t/kSNVqyRP1Rt

Can someone help me please, I am new in image processing and I need to convert theese dicom images into nifty, also I need to obtain for each image the nodule segmentation, also in a nifty format? Thks

I tried to extract 200 patient annotations and dicoms. However the pl.query gets very slow after a few loops

Hi!
Your library is amazing!

How can i cut the annotations from the base and save it as a new image (just the annotations) using pylidc? Is it possible?

Hi,

I would like to know if there's any part of the codes I can change to concentrate on annotated non-nodules by the radiologist on the LIDC dataset? The idea is to preprocess these non-nodules as crops alongside with the nodules. Would appreciate if I could get some help on this.

The root folder is not accessible in colab, so how do I access the configuration file to edit it? Should I create the file?

Keep receiving the same error: "RuntimeError: Could not establish path to dicom files. Have you specified the path option in the configuration file /root/.pylidcrc?"

Hi,

I noticed the following metadata in LIDC-IDRI-0132/1.3.6.1.4.1.14519.5.2.1.6279.6001.218658642102832118810712329678/1.3.6.1.4.1.14519.5.2.1.6279.6001.151647338241909635299641922057/000000.dcm:

[0040,0275]	RequestAttributesSequence		SQ	94
	[fffe,e000]	Item		na	78
		[0040,0007]	ScheduledProcedureStepDescription	CT, CHEST, W CONTRAST	LO	22

It's writtent W CONTRAST , yet pylidc returns contrast_used == False.

I think pylidc is correct since there's not Contrast/Bolus Agent dicom field, but i'm not sure...

any opinion?

thanks

Hi, I had a question about how to go through a patient with multiple nodules. For example, I can see the first nodule of patient #1 and each annotation mask/slice to go along with it, but if patient 1 has multiple nodules each with different annotations is there any way I can check that?

Precisely, if a patient has more than 1 nodule, how would I know they have more than 1 and be able to access the other nodules? Thank you.

Annotation(id=3761,scan_id=516) has internalStructure 5 which should not be greater than 4.
I checked the xml file of LIDC-IDRL-0516, there are only three <internalStructure>1</internalStructure> tag.

here is the code

import pylidc as pl
anno = pl.query(pl.Annotation).filter(pl.Annotation.id==3761).first()
print(anno.internalStructure)
print(anno.InternalStructure)

5
Backend TkAgg is interactive backend. Turning interactive mode on.
Traceback (most recent call last):
  File "C:\Users\p-c\.conda\envs\pylidc\lib\runpy.py", line 194, in _run_module_as_main
    return _run_code(code, main_globals, None,
  File "C:\Users\p-c\.conda\envs\pylidc\lib\runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "c:\Users\p-c\.vscode\extensions\ms-python.python-2022.6.0\pythonFiles\lib\python\debugpy\__main__.py", line 45, in <module>    cli.main()
  File "c:\Users\p-c\.vscode\extensions\ms-python.python-2022.6.0\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 444, in main
    run()
  File "c:\Users\p-c\.vscode\extensions\ms-python.python-2022.6.0\pythonFiles\lib\python\debugpy/..\debugpy\server\cli.py", line 285, in run_file
    runpy.run_path(target_as_str, run_name=compat.force_str("__main__"))
  File "C:\Users\p-c\.conda\envs\pylidc\lib\runpy.py", line 265, in run_path
    return _run_module_code(code, init_globals, run_name,
  File "C:\Users\p-c\.conda\envs\pylidc\lib\runpy.py", line 97, in _run_module_code
    _run_code(code, mod_globals, init_globals,
  File "C:\Users\p-c\.conda\envs\pylidc\lib\runpy.py", line 87, in _run_code
    exec(code, run_globals)
  File "I:\ywt\igniteTorch\test1.py", line 5, in <module>
    print(anno.InternalStructure)
  File "C:\Users\p-c\.conda\envs\pylidc\lib\site-packages\pylidc\Annotation.py", line 214, in InternalStructure
    assert s in range(1,5), "Internal structure score out of bounds."
AssertionError: Internal structure score out of bounds.

python version
Python 3.8.18
pylidc 0.2.3

@notmatthancock I was (again) reading this paper, and came across the example shown below (Fig.8):

Armato et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans. Med. Phys. 38, 915–931 (2011). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3041807/

What is expected from the clustering of annotations into nodules, as implemented in pylidc, in this situation? I do not know which case was used in that illustration, so cannot quickly check what is actually happening.

I wanted to re-generate the database to include some additional attributes (#12 is a side-effect of trying to do that), but ran into this error:

$ python populate.py                                                                                     
Traceback (most recent call last):
  File "populate.py", line 70, in <module>
    pl._session.add(pl._Configuration(key='path_to_dicom_files', value=dicom_root_path))
AttributeError: module 'pylidc' has no attribute '_Configuration'

Was pl._Configuration left out by mistake?

Reading the annotated XML file document from the LIDC-IDRI collection page, it says the following:

signifies beginning of region of interest description roi is a boundary describing either:
(a) a single voxel for nodules < 3 mm
(b) a complete contour of the nodule that describes the first voxel just outside the nodule.

I am hopefully missing something, but I cannot find the place in the code where the fact that the XML-defined contour is outside the nodule is accounted for. It looks like in pylidc the points of the contour are treated as those that are inside the nodule.

Would be great if it could be confirmed whether the contour points are not considered part of the nodule.

If a user specifies a non-existent DICOM path in the pylidc config file, an error message is shown. This error message would be more helpful if it included the DICOM path that cannot be located from the config file:

Hello, when I try to use scan.visualize i keep getting this error:

Loading dicom files ... This may take a moment.
Traceback (most recent call last):
File "pylidc_test.py", line 6, in
scan.visualize(annotation_groups=nodules)
File "C:\Users\hbrum\AppData\Local\Programs\Python\Python37-32\lib\site-packages\pylidc\Scan.py", line 542, in visualize
for cell in scan_info_table.properties()['child_artists']:
KeyError: 'child_artists'

Sorry if this is a dumb question but I'm just getting started with Pylidc and I'm having trouble using the Annotation class to get the masks of different LIDC scans.
The code says
ann = pl.query(pl.Annotation).first()
but as far as I can tell, this only gets the scan for the LIDC-0078 data. How would I modify this to run the scan for other LIDC data? Using .filter(Patient ID) does not work either. Thank you

Hi, I'm so sorry to bother you. I have tried to visualize the mask and slices through the ann.visualize_in_3d(), but the output is just a three-dimensional image shown in my text editor. Therefore, I am wondering if there are actual 3d data in the LIDC datasets that I can interact with in different formats. Furthermore, is there a way that I can extract and save the 3d visualization on my computer just like what I can do for the slice and mask images?

how can we obtain the physical bbox? (coordinate position) @notmatthancock
Is there a tutorial that can be a reference for me?

thanks

When plotting scans, annotations or contours, plotting functions expect a 000000.dcm file when LIDC-IRC dataset does not have them.

Bug related to plotting methods: scan.visualize(), ann.visualize_in_scan() and ann.visualize_in_3d()

Error trace:

---

FileNotFoundError Traceback (most recent call last)
in ()
1 ann = pl.query(pl.Annotation).first()
----> 2 vol, seg = ann.uniform_cubic_resample(side_length = 100)
3 print(vol.shape, seg.shape)
4 # => (101, 101, 101) (101, 101, 101)
5

/home/plexadmin/.virtualenvs/data-science-bowl-2017/lib/python3.5/site-packages/pylidc/Annotation.py in uniform_cubic_resample(self, side_length, verbose)
750
751 # Load the images. Get the z positions.
--> 752 images = self.scan.load_all_dicom_images(verbose=verbose)
753 img_zs = [float(img.ImagePositionPatient[-1]) for img in images]
754 img_zs = np.unique(img_zs)

/home/plexadmin/.virtualenvs/data-science-bowl-2017/lib/python3.5/site-packages/pylidc/Scan.py in load_all_dicom_images(self, verbose)
308 images = []
309 for dicom_file_name in sorted_fnames:
--> 310 with open(os.path.join(path, dicom_file_name), 'rb') as f:
311 images.append( dicom.read_file(f) )
312 return images

FileNotFoundError: [Errno 2] No such file or directory: '~/lidc_idri/data/LIDC/DOI/LIDC-IDRI-0078/1.3.6.1.4.1.14519.5.2.1.6279.6001.339170810277323131167631068432/1.3.6.1.4.1.14519.5.2.1.6279.6001.303494235102183795724852353824/000000.dcm'

Whenever I try to visualise any of the scans, I get an error as follows :

I have made the .pylidcrc file in the required location (/Users/shravaniurala/.pylidcrc) and have also mentioned the path to my dataset in it. The dataset is in an external hard drive

Please help me solve the same since I have tried to solve it multiple times by ensuring the file is in the correct location.

ImportError: Cannot import name "marching_cubes_lewiner' from skimage.measure'

Hi, I really want to know if it is possible to give the path of a scan as "./LIDC-IDRI/LIDC-IDRI-0001" and return the .dcm that has more than 3 annotations and the .dcm that has 0 annotation, this is to separate the dicoms of a certain patient into "image with nodule" and "image without nodules". I would appreciate if you could get help with this and thanks in advance.

Hi,

Is there a way to access dicom attributes through pylidc?

Thanks

I am confused about the behavior of the query below. Should it filter by patient ID, or not?

Hi, I print out this bounding box for first annotation as mentioned in tutorial. But I couldn't figure out the physical meaning of values in each of these tuple : slice(151, 185, None). Could you please help me understand it's meaning.
Thank you

import pylidc as pl

ann = pl.query(pl.Annotation).first()
print(ann.bbox())

=> (slice(151, 185, None), slice(349, 376, None), slice(44, 50, None))

Hi,

I'm working on the LIDC-IDRI dataset and your tool is really convenient, thanks a lot.

I wanted to know if the current pylidc.sqlite is up-to-date since the collection has been updated recently (2/24/2020)?

thanks

Hi, I am doing lung nodule predictions on the LIDC-IDRI-0069 sample, and I got the center and the size of the nodule detection to be
"center": [-59.54512023925781, 84.96267700195313, 1369.8218994140625],
"orientation": [-1.0, -0.0, -0.0, -0.0, -1.0, -0.0, 0.0, 0.0, 1.0],
"size": [9.868255615234377, 10.00336456298828, 19.705078125],

I am pretty sure that this lung nodule location corresponds to the first annotation, which is the one available if I run

pid = 'LIDC-IDRI-0069'
annotations = pl.query(pl.Annotation).filter(pl.Scan.patient_id == pid).first()
print("centre>>", annotations.centroid) => [169.19642857, 360.81122449, 46.20153061]
If I convert back to the real-world coordinate:
The following is the metadata
Spacing: [0.741, 0.741, 2.0] (in millimeters)
Origin (RAS): [184.3750, 188.2812, 1288.5]
169 * 0.741-184 = -58,
360 * 0.741-188=78,
46 * 2 + 1288.5 = 1380.5

This actually very much matches the coordinates provided by my prediction, however, I am adding the origin z value while I am minusing the origin x, y value in order to reproduce the value I predicted
This is not consistent, may I know is this formula correct?

I try to check on the second and third lungs nodules in the sample to cross check but I didn't find a way to get the second and third lung nodule annotations. I use this method from poe to get the second lung nodule but It doesn't seem right

pid = 'LIDC-IDRI-0069'
scan = pl.query(pl.Scan).filter(pl.Scan.patient_id == pid).first()
Query annotations for that specific scan
annotations = pl.query(pl.Annotation).filter(pl.Annotation.scan_id == scan.id).all()
second_annotation = annotations[1]

RuntimeError: Could not establish path to dicom files. Have you specified the path option in the configuration file /Users/gedelasnigda/.pylidcrc?
I am getting this runtime error

I have put this code in my configuration file:
[dicom]
path=/Users/snigda/Desktop/x/LIDC-IDRI
warn = True

Hi!

Thanks for this library -- it has been very useful!!

I was wondering if it is possible to change the path where the sqlite database is stored.

In the current code, the sqlite database is stored in the pylidc installation directory. This means the user must have permissions to write to this directory whenever they import pylidc. However this might not always be the case. For example consider a Dockerfile that looks like this:

USER root
pip install pylidc
USER userA
RUN python -c "import pylidc"

This will fail since userA does not have permissions to write to wherever the root user installed pylidc.

My current workaround is to install pylidc separately for every user. Instead, I was thinking we can change where the sqlite database is stored to a temporary directory like /tmp/pylidc.sqlite?

Thanks again for your help!

Regards,
Nand

When the configuration file .pylidrc doesn't exist, a warning message is printed. This message should be printed only initially and the configuration file created automatically with warn set to False.

i have specified the correct path of my dicom files but still i am getting this error. i hope you could help me in this.
i specified the path like this.

error is this

  File "/home/lungsegment/virtualenv/medic/lib/python3.7/site-packages/pylidc/Scan.py", line 644, in to_volume
    for x in images
  File "/home/lungsegment/virtualenv/medic/lib/python3.7/site-packages/pylidc/Scan.py", line 644, in <listcomp>
    for x in images
  File "/home/lungsegment/virtualenv/medic/lib/python3.7/site-packages/pydicom/dataset.py", line 1716, in pixel_array
    self.convert_pixel_data()
  File "/home/lungsegment/virtualenv/medic/lib/python3.7/site-packages/pydicom/dataset.py", line 1435, in convert_pixel_data
    self._convert_pixel_data_without_handler()
  File "/home/lungsegment/virtualenv/medic/lib/python3.7/site-packages/pydicom/dataset.py", line 1545, in _convert_pixel_data_without_handler
    raise last_exception
  File "/home/lungsegment/virtualenv/medic/lib/python3.7/site-packages/pydicom/dataset.py", line 1525, in _convert_pixel_data_without_handler
    self._do_pixel_data_conversion(handler)
  File "/home/lungsegment/virtualenv/medic/lib/python3.7/site-packages/pydicom/dataset.py", line 1554, in _do_pixel_data_conversion
    arr = handler.get_pixeldata(self)
  File "/home/lungsegment/virtualenv/medic/lib/python3.7/site-packages/pydicom/pixel_data_handlers/numpy_handler.py", line 317, in get_pixeldata
    .format(actual_length, padded_expected_len)
ValueError: The length of the pixel data in the dataset (524285 bytes) doesn't match the expected length (524288 bytes). The dataset may be corrupted or there may be an issue with the pixel data handler.

This error encounter when I try to convert LIDC-IDRI-0401 to numpy array

Hello

Thanks for this nice software!
In the LIDC dataset the contours are created from different radiologists.. Approximately 4 have independently annotated the nodules
In order to construct more reliable segmentations /contours it is necessary to discriminate which annotator has annotated the nodules. Is it possible to retrieve information the information about which contours belong to a specific annotator (1,2,3,4)?

Hey,
I'd like to continue my post from #9 here since it appears to me to be a real issue.
I'd expect bbox() to enclose an annotation in a cuboid.
However, from my POV this appears to be not the case:

import matplotlib.pyplot as plt
import numpy as np
import pylidc as pl
from matplotlib.widgets import Slider


def cube_show_slider(cube, axis=2, is_mask=False, **kwargs):
    """
    Display a 3d ndarray with a slider to move along the third dimension.

    Extra keyword arguments are passed to imshow
    (Kudos to http://nbarbey.github.io/2011/07/08/matplotlib-slider.html)
    """
    import matplotlib.pyplot as plt
    from matplotlib.widgets import Slider

    # check dim
    if not cube.ndim == 3:
        raise ValueError("cube should be an ndarray with ndim == 3")

    # generate figure
    fig = plt.figure()
    subplot = fig.add_subplot(111)
    fig.subplots_adjust(left=0.25, bottom=0.25)

    if is_mask:
        im1 = subplot.imshow(cube[:, :, 0])
        fig.colorbar(im1)
    else:
        # select first image
        s = [slice(0, 1) if i == axis else slice(None) for i in range(3)]
        im = cube[s].squeeze()

        # display image
        l = subplot.imshow(im, **kwargs)

    # define slider
    axcolor = 'lightgoldenrodyellow'
    ax = fig.add_axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)

    slider = Slider(ax, 'Axis %i index' % axis, 0, cube.shape[axis] - 1, valinit=0, valfmt='%i')

    def update(val):
        if is_mask:
            subplot.imshow(cube[:, :, int(val)])
            fig.canvas.draw()
        else:
            ind = int(slider.val)
            s = [slice(ind, ind + 1) if i == axis else slice(None)
                 for i in range(3)]
            im = cube[s].squeeze()
            l.set_data(im, **kwargs)
        fig.canvas.draw()

    slider.on_changed(update)

    plt.show()


def display_training_pair(input_cube, output_cube, axis=2, **kwargs):
    # check dims
    if not input_cube.ndim == 3 or not output_cube.ndim == 3:
        raise ValueError("cube should be an ndarray with ndim == 3")

    # generate figure
    fig = plt.figure()
    subplot_input = fig.add_subplot(221)
    subplot_output = fig.add_subplot(222)
    fig.subplots_adjust(left=0.25, bottom=0.25)

    # select first image
    im1 = subplot_output.imshow(output_cube[:, :, 0])
    fig.colorbar(im1)

    s = [slice(0, 1) if i == axis else slice(None) for i in range(3)]
    im = input_cube[s].squeeze()
    # display image
    l = subplot_input.imshow(im, **kwargs)

    # define slider
    axcolor = 'lightgoldenrodyellow'
    ax = fig.add_axes([0.25, 0.1, 0.65, 0.03], axisbg=axcolor)

    slider = Slider(ax, 'Axis %i index' % axis, 0, input_cube.shape[axis] - 1, valinit=0, valfmt='%i')

    def update(val):
        subplot_output.imshow(output_cube[:, :, int(val)])

        ind = int(slider.val)
        s = [slice(ind, ind + 1) if i == axis else slice(None)
             for i in range(3)]
        im = input_cube[s].squeeze()
        l.set_data(im, **kwargs)
        fig.canvas.draw()

    slider.on_changed(update)

    plt.show()


if __name__ == '__main__':
    scan = pl.query(pl.Scan).filter(pl.Scan.patient_id == 'LIDC-IDRI-0001').first()
    Z_RANGE = 340  # (from -340 to 0)
    SLICES = 133
    scan_volume = scan.to_volume()
    scan_volume_mask = np.zeros(scan_volume.shape)
    an = scan.annotations[0]
    an_box = an.bbox()
    z_start = int((Z_RANGE + an_box[2, 0]) / Z_RANGE * SLICES)
    z_end = int((Z_RANGE + an_box[2, 1]) / Z_RANGE * SLICES)
    scan_volume_mask[int(an_box[0, 0]):int(an_box[0, 1]),
                     int(an_box[1, 0]):int(an_box[1, 1]),
                     int(z_start):int(z_end)] = 1

    print("Expected outcome when mask is applied to lung image:")
    vol, seg = an.uniform_cubic_resample(49)
    cube_show_slider(vol * seg, is_mask=True)

    print("Current outcome:")
    display_training_pair(scan_volume, scan_volume_mask)
    cube_show_slider(scan_volume * scan_volume_mask, is_mask=True)

... produces the correct expected result when the binary mask is applied to the data (here using uniform_cubic_resample):

but one can see that the binary mask positioned using the bbox() coordinates is not at the exact same position as the annotation

which becomes even clearer when the mask is applied to the data and the annotation is not included in the masked image

Am I doing something wrong?
All I want is a binary image whose pixels are 1 if there is a cancerous annotation and otherwise 0 while the image shape is the same as the one of scan.to_volume().

Hi,
I am working on this library from Google Colab. I know how to add a path from Google Colab. But where do I have to mention the respective path in the Scan Class?
Also, I have categorized negative and positive images under the same hierarchy of LIDC, e.g.
/content/drive/My Drive/LIDC-IDRI-0001/01-01-2000-30178/3000566-03192/Positive/000003

Its not working!