Generation of 82146 chromosomal overlappings from pairs of chromosomes

The following jupyter notebook was published on github . The aim is to generate a large dataset of overlapping chromosomes (grey scaled image + ground truth label image) to train a neural network to perform semantic segmentation on such images.
To gain a large number of images the resolution was decreased by 16. A first try proposed in the ai.on project seems to do a very good job. The results was obtained from a dataset of 13434 pair of images from a python implementation of Unet.

DeepFISH : a github repository where to find datasets, notebooks and raw images

For the opening of the topic about chromosomes segmentation on AI.ON, a github repository, DeepFISH (Sorry for the name) was created.

Today, this repo contains:

• datasets: hope to train some kind of convolution neural network to perform semantic segmentation to resolve overlapping chromosomes. The two datasets modelize the overlapping of two chromosomes.

 

• notebooks: for generating the dataset and for loading and displaying the datasets. The jupyter notebooks are written in python 3. Initially the notebook used to generate the datasets was written in python2.

 

• raw images: This is additional raw images of metaphasic chromosomes staines with DAPI and labelled with a telomeric probe (PNA-Cy3).

chromosome segmentation problem on AI.ON

The problem of chromosomes segmentation is exposed on AI.ON.
Some data for possibly training an ANN are available at kaggle and the way to produce them too.

An example of modelization of overlapping chromosomes

example of generation of overlapping chromosomes.Left: greyscale image (DAPI+Telomeres). Right: red label maps to pixels belonging to the overlapping domain, blue and yellow maps to pixel belonging to single chromosomes.

This image is used for illustration on kagle.

Generating images of overlapping chromosomes

Generating examples of two overlapping chromosome from single chromosomes: 

Notebook:

A notebook showing the different steps to generate overlapping examples is available on kaggle. As well as the raw images.

The idea:

Images of single chromosomes hybridized with with telomeric Cy3-probe and DAPI counterstained were extracted from the image of a metaphase:

DAPI stained chromesomes (left), (CCCTAA)3-PNA-Cy3  probes (Red)

The monochrome DAPI and Cy3 images were added. Single chromosomes were isolated:

All images are 131x128

Given two single chromosomes, examples of overlapping chromosomes were generated combining rotations and translations:

Each greyscale image of a pair of overlapping chromosomes, is associated with a mask containing labels. The pixels of value equal to 3 maps to the overlapping domain of the two chromosomes:

Then a dataset containing 2853 examples of overlapping chromosomes was generated and saved as a h5f file available here:

 examples of overlapping chromosomes 

Once downloaded and uncompressed, the examples can be loaded as a hdf5 file as shown bellow:

Modelling intersection between polygons with geogebra.

Rotate the polygons using the points B and I. Move the yellow polygon with the sliders v and w

Using TensorFlow 0.7 from python on a laptop running Manjaro Linux.

The use of GPU version of tensorflow is tested on a laptop running manjaro Linux distribution.

Checking the Nvidia driver installation:

First, check the GPU presence

$ lspci -k | grep -A 2 -E "(VGA|3D)"

00:02.0 VGA compatible controller: Intel Corporation 4th Gen Core Processor Integrated Graphics Controller (rev 06)

        Subsystem: CLEVO/KAPOK Computer Device 5000

        Kernel driver in use: i915

--

01:00.0 3D controller: NVIDIA Corporation GK208M [GeForce GT 740M] (rev ff)

        Kernel modules: nouveau, nvidia

03:00.0 Network controller: Intel Corporation Wireless 7260 (rev 73)

Check if the GPU can be harnessed

 For example with using glxgears. Without GPU, glxgears yields:

$ glxgears

Running synchronized to the vertical refresh.  The framerate should be

approximately the same as the monitor refresh rate.

453 frames in 5.0 seconds = 90.581 FPS

302 frames in 5.0 seconds = 60.288 FPS

XIO:  fatal IO error 11 (Resource temporarily unavailable) on X server ":0"

      after 3146 requests (3146 known processed) with 0 events remaining.

Now let's run glxgears with the gpu acceleration:

$ optirun glxgears

9891 frames in 5.0 seconds = 1977.995 FPS

10186 frames in 5.0 seconds = 2037.131 FPS

9953 frames in 5.0 seconds = 1990.527 FPS

9908 frames in 5.0 seconds = 1981.540 FPS

10129 frames in 5.0 seconds = 2025.626 FPS

glxgears is accelerated by a factor 20 at least.

Installing CUDA 7.5 and cuDNN

CUDA 7.5  was installed from manjaro repository using the Octopi GUI. cuDNN was installed using Octopi. This library has to be first downloaded from the nvidia developer website.

In manjaro Linux, CUDA is installed in /opt/cuda/. To check cuda installation, samples files were built under user directory as explained from Nvidia documentation.

Copy the directory /opt/cuda/samples into your home dir ~/HOME, example:

cp -r /opt/cuda/samples/ ~jeanpat/

Build (with make or make -j4  with a 4 cores CPU ) the test applications in the /samples directory:

$ ls -l
total 144
drwxr-xr-x 48 jeanpat users  4096 11 mai   12:10 0_Simple
drwxr-xr-x  7 jeanpat users  4096 11 mai   12:10 1_Utilities
drwxr-xr-x 12 jeanpat users  4096 11 mai   12:10 2_Graphics
drwxr-xr-x 21 jeanpat users  4096 11 mai   12:10 3_Imaging
drwxr-xr-x 10 jeanpat users  4096 11 mai   12:10 4_Finance
drwxr-xr-x 10 jeanpat users  4096 11 mai   12:10 5_Simulations
drwxr-xr-x 31 jeanpat users  4096 11 mai   12:10 6_Advanced
drwxr-xr-x 37 jeanpat users  4096 11 mai   12:10 7_CUDALibraries
drwxr-xr-x  6 jeanpat users  4096 11 mai   12:10 common
-rw-r--r--  1 jeanpat users 96407 11 mai   12:10 EULA.txt
-rw-r--r--  1 jeanpat users  2652 11 mai   12:10 Makefile

The binary file deviceQuery can be launched using optirun:

$ optirun ./deviceQuery
./deviceQuery Starting...

 CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "GeForce GT 740M"
  CUDA Driver Version / Runtime Version          7.5 / 7.5
  CUDA Capability Major/Minor version number:    3.5
  Total amount of global memory:                 2048 MBytes (2147352576 bytes)
  ( 2) Multiprocessors, (192) CUDA Cores/MP:     384 CUDA Cores
  GPU Max Clock rate:                            1032 MHz (1.03 GHz)
  Memory Clock rate:                             900 Mhz
  Memory Bus Width:                              64-bit
  L2 Cache Size:                                 524288 bytes
  Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
  Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
  Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
  Total amount of constant memory:               65536 bytes
  Total amount of shared memory per block:       49152 bytes
  Total number of registers available per block: 65536
  Warp size:                                     32
  Maximum number of threads per multiprocessor:  2048
  Maximum number of threads per block:           1024
  Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
  Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
  Maximum memory pitch:                          2147483647 bytes
  Texture alignment:                             512 bytes
  Concurrent copy and kernel execution:          Yes with 1 copy engine(s)
  Run time limit on kernels:                     Yes
  Integrated GPU sharing Host Memory:            No
  Support host page-locked memory mapping:       Yes
  Alignment requirement for Surfaces:            Yes
  Device has ECC support:                        Disabled
  Device supports Unified Addressing (UVA):      Yes
  Device PCI Domain ID / Bus ID / location ID:   0 / 1 / 0
  Compute Mode:
     < Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 7.5, CUDA Runtime Version = 7.5, NumDevs = 1, Device0 = GeForce GT 740M
Result = PASS

Running tensorflow 0.7 from python

Tensorflow was installed (version with GPU acceleration) according to the documentation in a virtual environment. Then the latest version can be installed using:

pip install --upgrade tensorflow

According to the documentation, the tensorflow installation can be checked as follow:

$ python
...
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print(sess.run(a + b))
42
>>>

However, to run properly the python interpreter must be run with optirun:

$ optirun python
Python 2.7.11 (default, Mar  3 2016, 11:00:04)
[GCC 5.3.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:900] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
name: GeForce GT 740M
major: 3 minor: 5 memoryClockRate (GHz) 1.0325
pciBusID 0000:01:00.0
Total memory: 2.00GiB
Free memory: 1.97GiB
I tensorflow/core/common_runtime/gpu/gpu_init.cc:126] DMA: 0
I tensorflow/core/common_runtime/gpu/gpu_init.cc:136] 0:   Y
I tensorflow/core/common_runtime/gpu/gpu_device.cc:717] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GT 740M, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 2.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 4.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 8.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 16.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 32.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 64.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 128.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 256.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 512.0KiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 2.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 4.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 8.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 16.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 32.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 64.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 128.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 256.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 512.00MiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 1.00GiB
I tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:51] Creating bin of max chunk size 2.00GiB
>>> print sess.run(hello)
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print sess.run(a+b)
42
>>> 

 Conclusion

At first sight, the issue of the use of CUDA 7.5 with TensorFlow is resolved with TensorFlow 0.7.

Correction of uneven illumination of images of cattle nuclei at low magnification

Building opencv 3.1 under Manjaro linux

As previously, opencv was build using cmake-gui to set-up the build options. CUDA 7.5 was installed from manjaro files repository with octopi files manager.

Everything went fine during compilation with: make -j4 followed by sudo make install.

However when importing opencv from a python console, things went wrong:

$ python2
Python 2.7.11 (default, Dec  6 2015, 15:43:46)
[GCC 5.2.0] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import cv2
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ImportError: No module named cv2

I didn't met this problem under Ubuntu but the problem is known. Specifying the path to cv2 from python allows the lib to be loaded:
 
>>> import sys
>>>sys.path.append('/usr/local/lib/python2.7/site-packages')
>>> import cv2
>>> cv2.__version__
'3.1.0'