ROIs segmented using ImageJ cannot be converted

[just-meng]

My ROIs are manual segmented using ImageJ and exported as a RoiSet.zip file. I used this script to run FISSA, and run into the following error. The ROIs seem to cause problems and cannot be converted to masks.

Traceback (most recent call last):
  File "/media/meng/Data/Meng_Psychedelics/02_code/02_prism_soma_dendrite/neuropil_correction.py", line 81, in <module>
    experiment.separate()
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/fissa/core.py", line 1364, in separate
    self.separation_prep(redo_prep)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/fissa/core.py", line 1246, in separation_prep
    outputs = Parallel(
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/joblib/parallel.py", line 1061, in __call__
    self.retrieve()
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/joblib/parallel.py", line 938, in retrieve
    self._output.extend(job.get(timeout=self.timeout))
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/multiprocessing/pool.py", line 774, in get
    raise self._value
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/multiprocessing/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/joblib/_parallel_backends.py", line 595, in __call__
    return self.func(*args, **kwargs)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/joblib/parallel.py", line 263, in __call__
    return [func(*args, **kwargs)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/joblib/parallel.py", line 263, in <listcomp>
    return [func(*args, **kwargs)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/fissa/core.py", line 209, in extract
    base_masks = datahandler.rois2masks(rois, curdata)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/fissa/extraction.py", line 124, in rois2masks
    return roitools.rois2masks(rois, cls.get_frame_size(data))
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/fissa/roitools.py", line 478, in rois2masks
    return getmasks(rois, shape)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/fissa/roitools.py", line 400, in getmasks
    mask = poly2mask(rois[i], shpe)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/fissa/polygons.py", line 70, in poly2mask
    polygons = _reformat_polygons(polygons)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/fissa/polygons.py", line 137, in _reformat_polygons
    Polygon(polygons[0])
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/shapely/geometry/polygon.py", line 230, in __new__
    shell = LinearRing(shell)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/shapely/geometry/polygon.py", line 104, in __new__
    geom = shapely.linearrings(coordinates)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/shapely/decorators.py", line 77, in wrapped
    return func(*args, **kwargs)
  File "/home/meng/anaconda3/envs/2p/lib/python3.10/site-packages/shapely/creation.py", line 173, in linearrings
    return lib.linearrings(coords, out=out, **kwargs)
ValueError: linearrings: Input operand 0 does not have enough dimensions (has 1, gufunc core with signature (i, d)->() requires 2)
    [Extraction 1/3] Converting ROIs to masks

However, when I load the RoiSet.zip and manually convert it into a list of Boolean masks, it works. It is the very same set of ROIs, though.

Is this a bug about specifically dealing with ImageJ .zip files? Can someone fix it?

[EmrickLab]

Hello! I've run into the exact same issue. Do you @just-meng mind explaining to me how to convert the RoiSet.zip to a boolean mask list that's compatible with FISSA? This is what I tried ~

import numpy as np
import tifffile
import roifile
from roifile import ImagejRoi

def roi_to_mask(tiff_path, roi_path):
    
    # get tif dimensions
    with tifffile.TiffFile(tiff_path) as tif:
        width, height = tif.pages[0].shape
        first_image = tif.pages[0].asarray()
    
    maskList = []

    roiList = ImagejRoi.fromfile(roi_path)
    for roi in roiList:
        coordinates = roi.coordinates()                 # roi boundary coordinates
        mask = np.zeros((height, width), dtype=bool)    # empty mask
        
        for x, y in coordinates:
            mask[int(y), int(x)] = True                 # fill mask 

        maskList.append(mask)
    
    return maskList, first_image

masks, img = roi_to_mask(tiff_path, rois_path)

import fissa
experiment = fissa.Experiment([tiff_path],
                              masks,
                              verbosity = 6,
                              nRegions=4)
experiment.separate()

I get TypeError: Wrong ROIs input format: expected a list or sequence, but got a <class 'numpy.ndarray'>

[just-meng]

I think all you have to do is to put masks in a list:

experiment = fissa.Experiment([tiff_path],
                              [masks],
                              verbosity = 6,
                              nRegions=4)

Btw I believe the way you create the mask only returns the boundary of the ROI but not the entire area. Maybe try this:

from skimage.draw import polygon
...
# inside your for loop
mask = np.zeros((height, width), dtype=bool)    # empty mask

# instead of this
# for x, y in coordinates:
#     mask[int(y), int(x)] = True                 # fill mask 

# do that
rr, cc = polygon(coordinates[:, 1], coordinates[:, 0], shape=(height, width))
mask[rr, cc] = True

maskList.append(mask)
...

You can always visually check the result, e.g. by plt.imshow(mask) to see if the entire ROI is masked.

Hope this helps! :)

[EmrickLab]

Thank you for the kind explanation @just-meng ! You're right, after sending the code I realized that the only thing I masked was the boundary. I have implemented your solution to fill the entire boundary accordingly.

I have indeed tried inputting the var masks as a list. Both list and unlisted inputs yield TypeErrors from the same script rois2masks(). Bane of my existence! Thanks again for the quick reply.

[just-meng]

That's strange. Can you post the error you get with [masks]?

[EmrickLab]

Sure! Here's my full script:

import numpy as np
import tifffile
import roifile
from roifile import ImagejRoi
from skimage.draw import polygon
import os

# set dir
path = os.path.expanduser("~" + os.sep + "Desktop")
os.chdir(path)

tiff_path = './GCaMP_AD.tif'
rois_path = './GC_AD_ROI.zip'

def roi_to_mask(tiff_path, roi_path):
    
    # get tif dimensions
    with tifffile.TiffFile(tiff_path) as tif:
        width, height = tif.pages[0].shape
        first_image = tif.pages[0].asarray()
    
    maskList = []

    roiList = ImagejRoi.fromfile(roi_path)
    n_roi = len(roiList)
    
    for roi in roiList:
        coordinates = roi.coordinates()                 # roi boundary coordinates
        mask = np.zeros((height, width), dtype=bool)    # empty mask
                   
        rr, cc = polygon(coordinates[:, 1], coordinates[:, 0], shape=(height, width))
        mask[rr, cc] = True

        maskList.append(mask)
    
    return maskList, first_image, n_roi

masks, img, n_roi = roi_to_mask(tiff_path, rois_path)

import fissa
experiment = fissa.Experiment([tiff_path],
                              [masks],
                              verbosity = 6,
                              nRegions=4)
experiment.separate()

Below is the full output:

Adopted default values for 'expansion'
Doing region growing and data extraction for 1 trials...
  Images:
    ./GCaMP_AD.tif
  ROI sets:
    [array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]]), array([[False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       ...,
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False],
       [False, False, False, ..., False, False, False]])]
  nRegions: 4
  expansion: 1
[Extraction 1/1] Extraction starting (./GCaMP_AD.tif)
    [Extraction 1/1] Loading imagery
[Parallel(n_jobs=-1)]: Using backend ThreadingBackend with 12 concurrent workers.
    [Extraction 1/1] Converting ROIs to masks
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_71300\275886425.py in <module>
     43                               verbosity = 6,
     44                               nRegions=4)
---> 45 experiment.separate()

~\anaconda3\envs\pWGA\lib\site-packages\fissa\core.py in separate(self, redo_prep, redo_sep)
   1362         # Do data preparation
   1363         if redo_prep or self.raw is None:
-> 1364             self.separation_prep(redo_prep)
   1365         if redo_prep:
   1366             redo_sep = True

~\anaconda3\envs\pWGA\lib\site-packages\fissa\core.py in separation_prep(self, redo)
   1252                     total=n_trial,
   1253                     desc="Extracting traces",
-> 1254                     disable=disable_progressbars,
   1255                 )
   1256             )

~\anaconda3\envs\pWGA\lib\site-packages\joblib\parallel.py in __call__(self, iterable)
   1054 
   1055             with self._backend.retrieval_context():
-> 1056                 self.retrieve()
   1057             # Make sure that we get a last message telling us we are done
   1058             elapsed_time = time.time() - self._start_time

~\anaconda3\envs\pWGA\lib\site-packages\joblib\parallel.py in retrieve(self)
    933             try:
    934                 if getattr(self._backend, 'supports_timeout', False):
--> 935                     self._output.extend(job.get(timeout=self.timeout))
    936                 else:
    937                     self._output.extend(job.get())

~\anaconda3\envs\pWGA\lib\multiprocessing\pool.py in get(self, timeout)
    655             return self._value
    656         else:
--> 657             raise self._value
    658 
    659     def _set(self, i, obj):

~\anaconda3\envs\pWGA\lib\multiprocessing\pool.py in worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
    119         job, i, func, args, kwds = task
    120         try:
--> 121             result = (True, func(*args, **kwds))
    122         except Exception as e:
    123             if wrap_exception and func is not _helper_reraises_exception:

~\anaconda3\envs\pWGA\lib\site-packages\joblib\_parallel_backends.py in __call__(self, *args, **kwargs)
    593     def __call__(self, *args, **kwargs):
    594         try:
--> 595             return self.func(*args, **kwargs)
    596         except KeyboardInterrupt as e:
    597             # We capture the KeyboardInterrupt and reraise it as

~\anaconda3\envs\pWGA\lib\site-packages\joblib\parallel.py in __call__(self)
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    262             return [func(*args, **kwargs)
--> 263                     for func, args, kwargs in self.items]
    264 
    265     def __reduce__(self):

~\anaconda3\envs\pWGA\lib\site-packages\joblib\parallel.py in <listcomp>(.0)
    261         with parallel_backend(self._backend, n_jobs=self._n_jobs):
    262             return [func(*args, **kwargs)
--> 263                     for func, args, kwargs in self.items]
    264 
    265     def __reduce__(self):

~\anaconda3\envs\pWGA\lib\site-packages\fissa\core.py in extract(image, rois, nRegions, expansion, datahandler, verbosity, label, total)
    207     if verbosity >= 3:
    208         print("{}Converting ROIs to masks".format(mheader))
--> 209     base_masks = datahandler.rois2masks(rois, curdata)
    210 
    211     # get the mean image

~\anaconda3\envs\pWGA\lib\site-packages\fissa\extraction.py in rois2masks(cls, rois, data)
    122         fissa.roitools.getmasks, fissa.roitools.readrois
    123         """
--> 124         return roitools.rois2masks(rois, cls.get_frame_size(data))
    125 
    126     @abc.abstractmethod

~\anaconda3\envs\pWGA\lib\site-packages\fissa\roitools.py in rois2masks(rois, shape)
    481         return rois
    482 
--> 483     raise ValueError("Wrong ROIs input format: unfamiliar shape.")

ValueError: Wrong ROIs input format: unfamiliar shape.

If I simply call masks instead of [masks], I get the error stated previously:

~\anaconda3\envs\pWGA\lib\site-packages\fissa\roitools.py in rois2masks(rois, shape)
    471         raise TypeError(
    472             "Wrong ROIs input format: expected a list or sequence, but got"
--> 473             " a {}".format(rois.__class__)
    474         )
    475 

TypeError: Wrong ROIs input format: expected a list or sequence, but got a <class 'numpy.ndarray'>

Thus, I tried changing my function in the following way:
maskList.append(mask) --> maskList.append(mask.tolist())

This I think made things better since the error moved a few lines further down lol. It seems like FISSA has an issue with my np boolean masks, so I converted the np arrays to lists. Now it's only worried about the tuples. Again, inputing masks as a list [masks] yields the same TypeError of 'unfamiliar shape'

~\anaconda3\envs\pWGA\lib\site-packages\fissa\roitools.py in rois2masks(rois, shape)
    475 
    476     # If it's a something by 2 array (or vice versa), assume polygons
--> 477     if np.shape(rois[0])[1] == 2 or np.shape(rois[0])[0] == 2:
    478         return getmasks(rois, shape)
    479     # If it's a list of bigger arrays, assume masks

IndexError: tuple index out of range

[just-meng]

I used your code and got no error!

Literally only changed the tiff_path and rois_path and the code ran through with no complaints!

The output is too long, but here is the finishing statement:

Finished separating signals from 109 ROIs across 1 trials in 10.6 seconds

Process finished with exit code 0

[EmrickLab]

I am thoroughly defeated then haha. Just to double check -- you ran the code above with [tiff_path] [masks] both listed? I guess I'll just try to reinstall FISSA...perhaps I irreparably messed with some of the internal files while trying to troubleshoot. Wish I could finish separating signals from N rois across 1 trials right now. Thanks again for your help!

*Eh, reinstalling didnt work

[just-meng]

Yes with [tiff_path] [masks] both listed just as in your code. If you have a tiff size of 512 x 512 like me, you can try my roiset with your code. If it works, then perhaps the problem is how your export the ROIs from ImageJ.
RoiSet.zip

[EmrickLab]

I resized my tif file to 512x512 (it was smaller) and used your RoiSet -- it worked.

I have further news to report. First I thought it worked perhaps because you used polygon ROIs. But I believe you have a number of freehand ones. Then I thought, hey they kept the generic "RoiSet.zip" filename. Well guess what happened after I changed my roi zip filename from GC_AD_ROI.zip to RoiSet.zip? It worked : D

Immense thanks once again for taking the time to get me up and running with this package. Cheers!

[just-meng]

Also strange, but I'm glad that your code is now working also for you!

Could you maybe try reading the GC_AD_ROI.zip using a different function?

import roifile
roiList = roifile.roiread(roi_path) # insteadt of roiList = ImagejRoi.fromfile(roi_path)

[EmrickLab]

I tried your version of the roi import and it worked on the roi file named GC_AD_ROI.zip. However, I then re-tried my own version of the roi import on this same zip file and it worked.

What changed? Well here's the order of events: I had the original zip file GC_AD_ROI.zip that I ran my code on got errors which you helped me resolve. After renaming this file to RoiSet I get no errors. Then I open RoiSet in Fiji and save another copy of it as GC_AD_ROI.zip This copy works with both roi import functions ~ roiread & ImagejRoi.fromfile().

I was running all of this on a jupyter notebook. I got everything to work...restarted the kernel...things still work. All I can think of is there was something corrupted with the original file and somehow it got fixed just by renaming it?

It's 5:30am and this feels like a bad fever dream so I'm calling it a day. But thanks once again!

[just-meng]

Perhaps you are right with the file being corrupted. Good night!

[nathalierochefort]

Hi @just-meng and @EmrickLab,
is your issue solved?
We are routinely using ROIs from ImageJ without any issue.
Happy to help.

[kirolle]

I have the same default behavior. The solution suggested in this thread works, but the original issue should be investigated.
I used this RoiSet.Zip file, which led to the issue.
RoiSet.zip

[just-meng]

Hi @nathalierochefort, as @kirolle points out, loading ROIs segmented using ImageJ requires the workaround. I agree that it would be nice to troubleshoot the original issue or incorporate the workaround into the code of FISSA.