1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
|
%global _empty_manifest_terminate_build 0
Name: python-vollseg
Version: 10.8.9
Release: 1
Summary: Segmentation tool for biological cells of irregular size and shape in 3D and 2D.
License: BSD-3-Clause
URL: https://github.com/kapoorlab/vollseg/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dd/b4/0309484cd5351270c72b6bf12ffc4c5c1ea90de95347f2539dc9ec515111/vollseg-10.8.9.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-stardist
Requires: python3-scipy
Requires: python3-tifffile
Requires: python3-matplotlib
Requires: python3-napari
Requires: python3-cellpose-vollseg
Requires: python3-torch
Requires: python3-test-tube
Requires: python3-lightning
Requires: python3-tox
Requires: python3-pytest
Requires: python3-pytest-cov
%description
# VollSeg
[](https://travis-ci.com/github/kapoorlab/vollseg)
[](https://pypi.org/project/vollseg/)
[](https://github.com/kapoorlab/napari-vollseg/raw/main/LICENSE)
[](https://twitter.com/entracod)
3D segmentation tool for irregular shaped cells
## Installation
This package can be installed by
`pip install --user vollseg`
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
If you are building this from the source, clone the repository and install via
```bash
git clone https://github.com/kapoorlab/vollseg/
cd vollseg
pip install --user -e .
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
```
### Pipenv install
Pipenv allows you to install dependencies in a virtual environment.
```bash
# install pipenv if you don't already have it installed
pip install --user pipenv
# clone the repository and sync the dependencies
git clone https://github.com/kapoorlab/vollseg/
cd vollseg
pipenv sync
# make the current package available
pipenv run python setup.py develop
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
# you can run the example notebooks by starting the jupyter notebook inside the virtual env
pipenv run jupyter notebook
```
Access the `example` folder and run the cells.
## Algorithm
Schematic representation showing the segmentation approach used in VollSeg. First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise. Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labelled via connected components (C). We then obtain seeds from the centroids of labelled image in B, for each labelled region of C in order to create bounding boxes and centroids. If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D). Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain final cell segmentation result shown in (F). All images are displayed in Napari viewer with 3D display view.
## Example
To try the provided notebooks we provide an example dataset of Arabidopsis, [Binary Images](https://doi.org/10.5281/zenodo.5217367), [Raw Images](https://doi.org/10.5281/zenodo.5217394) and [Labelled images](https://doi.org/10.5281/zenodo.5217341) and trained models: [stardist](https://doi.org/10.5281/zenodo.5227304), [Denoising](https://doi.org/10.5281/zenodo.5227316), [U-Net](https://doi.org/10.5281/zenodo.5227301). For training the networks use this notebook in [Colab](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabTrainModel.ipynb). To train a denoising model using noise to void use this [notebook](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabN2VTrain.ipynb)
## Docker
A Docker image can be used to run the code in a container. Once inside the project's directory, build the image with:
~~~bash
docker build -t voll .
~~~
Now to run the `track` command:
~~~bash
# show help
docker run --rm -it voll
~~~
## Requirements
- Python 3.7 and above.
## License
Under MIT license. See [LICENSE](LICENSE).
## Authors
- Varun Kapoor <randomaccessiblekapoor@gmail.com>
- Claudia Carabaña
- Mari Tolonen
%package -n python3-vollseg
Summary: Segmentation tool for biological cells of irregular size and shape in 3D and 2D.
Provides: python-vollseg
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-vollseg
# VollSeg
[](https://travis-ci.com/github/kapoorlab/vollseg)
[](https://pypi.org/project/vollseg/)
[](https://github.com/kapoorlab/napari-vollseg/raw/main/LICENSE)
[](https://twitter.com/entracod)
3D segmentation tool for irregular shaped cells
## Installation
This package can be installed by
`pip install --user vollseg`
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
If you are building this from the source, clone the repository and install via
```bash
git clone https://github.com/kapoorlab/vollseg/
cd vollseg
pip install --user -e .
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
```
### Pipenv install
Pipenv allows you to install dependencies in a virtual environment.
```bash
# install pipenv if you don't already have it installed
pip install --user pipenv
# clone the repository and sync the dependencies
git clone https://github.com/kapoorlab/vollseg/
cd vollseg
pipenv sync
# make the current package available
pipenv run python setup.py develop
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
# you can run the example notebooks by starting the jupyter notebook inside the virtual env
pipenv run jupyter notebook
```
Access the `example` folder and run the cells.
## Algorithm
Schematic representation showing the segmentation approach used in VollSeg. First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise. Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labelled via connected components (C). We then obtain seeds from the centroids of labelled image in B, for each labelled region of C in order to create bounding boxes and centroids. If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D). Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain final cell segmentation result shown in (F). All images are displayed in Napari viewer with 3D display view.
## Example
To try the provided notebooks we provide an example dataset of Arabidopsis, [Binary Images](https://doi.org/10.5281/zenodo.5217367), [Raw Images](https://doi.org/10.5281/zenodo.5217394) and [Labelled images](https://doi.org/10.5281/zenodo.5217341) and trained models: [stardist](https://doi.org/10.5281/zenodo.5227304), [Denoising](https://doi.org/10.5281/zenodo.5227316), [U-Net](https://doi.org/10.5281/zenodo.5227301). For training the networks use this notebook in [Colab](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabTrainModel.ipynb). To train a denoising model using noise to void use this [notebook](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabN2VTrain.ipynb)
## Docker
A Docker image can be used to run the code in a container. Once inside the project's directory, build the image with:
~~~bash
docker build -t voll .
~~~
Now to run the `track` command:
~~~bash
# show help
docker run --rm -it voll
~~~
## Requirements
- Python 3.7 and above.
## License
Under MIT license. See [LICENSE](LICENSE).
## Authors
- Varun Kapoor <randomaccessiblekapoor@gmail.com>
- Claudia Carabaña
- Mari Tolonen
%package help
Summary: Development documents and examples for vollseg
Provides: python3-vollseg-doc
%description help
# VollSeg
[](https://travis-ci.com/github/kapoorlab/vollseg)
[](https://pypi.org/project/vollseg/)
[](https://github.com/kapoorlab/napari-vollseg/raw/main/LICENSE)
[](https://twitter.com/entracod)
3D segmentation tool for irregular shaped cells
## Installation
This package can be installed by
`pip install --user vollseg`
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
If you are building this from the source, clone the repository and install via
```bash
git clone https://github.com/kapoorlab/vollseg/
cd vollseg
pip install --user -e .
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
```
### Pipenv install
Pipenv allows you to install dependencies in a virtual environment.
```bash
# install pipenv if you don't already have it installed
pip install --user pipenv
# clone the repository and sync the dependencies
git clone https://github.com/kapoorlab/vollseg/
cd vollseg
pipenv sync
# make the current package available
pipenv run python setup.py develop
`mamba install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia`
# you can run the example notebooks by starting the jupyter notebook inside the virtual env
pipenv run jupyter notebook
```
Access the `example` folder and run the cells.
## Algorithm
Schematic representation showing the segmentation approach used in VollSeg. First, we input the raw fluorescent image in 3D (A) and preprocess it to remove noise. Next, we obtain the star convex approximation to the cells using Stardist (B) and the U-Net prediction labelled via connected components (C). We then obtain seeds from the centroids of labelled image in B, for each labelled region of C in order to create bounding boxes and centroids. If there is no seed from B in the bounding box region from U-Net, we add the new centroid (in yellow) to the seed pool (D). Finally, we do a marker controlled watershed in 3D using skimage implementation on the probability map shown in (E) to obtain final cell segmentation result shown in (F). All images are displayed in Napari viewer with 3D display view.
## Example
To try the provided notebooks we provide an example dataset of Arabidopsis, [Binary Images](https://doi.org/10.5281/zenodo.5217367), [Raw Images](https://doi.org/10.5281/zenodo.5217394) and [Labelled images](https://doi.org/10.5281/zenodo.5217341) and trained models: [stardist](https://doi.org/10.5281/zenodo.5227304), [Denoising](https://doi.org/10.5281/zenodo.5227316), [U-Net](https://doi.org/10.5281/zenodo.5227301). For training the networks use this notebook in [Colab](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabTrainModel.ipynb). To train a denoising model using noise to void use this [notebook](https://github.com/kapoorlab/VollSeg/blob/main/examples/Train/ColabN2VTrain.ipynb)
## Docker
A Docker image can be used to run the code in a container. Once inside the project's directory, build the image with:
~~~bash
docker build -t voll .
~~~
Now to run the `track` command:
~~~bash
# show help
docker run --rm -it voll
~~~
## Requirements
- Python 3.7 and above.
## License
Under MIT license. See [LICENSE](LICENSE).
## Authors
- Varun Kapoor <randomaccessiblekapoor@gmail.com>
- Claudia Carabaña
- Mari Tolonen
%prep
%autosetup -n vollseg-10.8.9
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-vollseg -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue Apr 11 2023 Python_Bot <Python_Bot@openeuler.org> - 10.8.9-1
- Package Spec generated
|
