dense_crf3d: No segm produced for single modality #2

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1. satrajitgithub

   changed the title [-]No segm produced for single modality[/-] [+]dense_crf3d: No segm produced for single modality[/+] on Aug 6, 2019
2. taigw commented on Dec 20, 2019

   

   taigw

   on Dec 20, 2019

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   @satrajitgithub @MENG1996 Not sure what problem you have. I have updated the code and a demo for single-modality segmentation is added. See examples/demo_densecrf3d.py for details.
3. sara-eb commented on May 5, 2020

   

   sara-eb

   on May 5, 2020

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   @taigw could you please explain what these parameters stand for? I need to post-process my data and do not know what changes should I do that best suits to my data.

   Hi taigw,

   I am trying to run dense_crf3d on single modality (only T1c).
   At first I used: dense_crf_param['BilateralModsStds'] = (3.0) but that didnt work. So I used dense_crf_param['BilateralModsStds'] = (3.0,) instead.
   These are the parameters I use now and the module runs as well:

       dense_crf_param = {}
       dense_crf_param['MaxIterations'] = 2.0
       dense_crf_param['PosRStd'] = 3.0
       dense_crf_param['PosCStd'] = 3.0
       dense_crf_param['PosZStd'] = 3.0
       dense_crf_param['PosW'] = 1.0
       dense_crf_param['BilateralRStd'] = 5.0
       dense_crf_param['BilateralCStd'] = 5.0
       dense_crf_param['BilateralZStd'] = 5.0
       dense_crf_param['ModalityNum'] = 1
       dense_crf_param['BilateralW'] = 3.0
       dense_crf_param['BilateralModsStds'] = (3.0,)
   

   But the segmentation produced is all zeros.

   I did a gridsearch with several random values of PosStds,PosWs,BilateralStds,BilateralWs but its all zero. Do you have any suggestions on this?

   I have a multi-class segmentation problem, once I am giving the probability map of all classes, the output is a blank image (an image with zero values).

   I have probability map of 4 classes (0: background, 1: object1, ..., 4: object4) and it has the shape of P: (166, 347, 82, 5) and I commented the line 31 to line 33, and I is float values between [0,1]. I commented out the line 27 and 28 in my case.

   Then I tried to send only one probability map:

       I = np.asarray([I1], np.uint8)            # (1, 166, 347, 82)
       I = np.transpose(I, [1, 2, 3, 0])           # (166, 347, 82, 1)
       # probability map for each class
       #P = 0.5 + (P - 0.5) * 0.8
       P = np.asarray([1.0 - P, P], np.float32)    # (2, 166, 347, 82)
       P = np.transpose(P, [1, 2, 3, 0])           # (166, 347, 82, 2)
   
       #pdb.set_trace()
       dense_crf_param = {}
       dense_crf_param['MaxIterations'] = 40.0   #2.0
   

   This is the output before sending to densCRF algorithm, and need to fill the holes and smooth the edges.
   
   and this is the probability map of single class that I am sending to densecrf3d
   

   and changed the number of iteration to 40.0.
   

   then I changed the iterations to 70, the output did not change:
   

   The questions are:

   • why the algorithm does not work for multi-class probability map? where I am doing mistake?

   • How to change the parameters, which parameters?

   Thank you
4. taigw commented on Aug 27, 2020

   

   taigw

   on Aug 27, 2020

   Last edited by taigw

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   @sara-eb I have now added a demo for multi-class segmentation, see examples/demo_densecrf.py
   To understand the meaning of each parameter, please check the original paper:
   Philipp Krähenbühl and Vladlen Koltun, "Efficient inference in fully connected crfs with gaussian edge potentials", in NIPS, 2011.
5. sneh-debug commented on Nov 11, 2020

   

   sneh-debug

   on Nov 11, 2020

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   hello @sara-eb are you able to figure out why its generating blank image?
6. JunMa11 commented on Mar 18, 2021

   

   JunMa11

   on Mar 18, 2021

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   @sara-eb Following is my personal understanding of the parameters.
   @taigw Please correct me if I’m wrong. Thank you for the awesome repository.

       dense_crf_param = {}
       dense_crf_param['MaxIterations'] = 2.0
       # weights of the unary potential
       dense_crf_param['PosW'] = 2.0
       # standard deviation in the kernel
       dense_crf_param['PosRStd'] = 5 
       dense_crf_param['PosCStd'] = 5
       dense_crf_param['PosZStd'] = 5
       # weights of the pairwise potential
       dense_crf_param['BilateralW'] = 3.0
       # standard deviation in the kernel (pairwise potential)
       dense_crf_param['BilateralRStd'] = 5.0
       dense_crf_param['BilateralCStd'] = 5.0
       dense_crf_param['BilateralZStd'] = 5.0
       dense_crf_param['ModalityNum'] = 1
       dense_crf_param['BilateralModsStds'] = (5.0,)
7. taigw commented on Sep 23, 2021

   

   taigw

   on Sep 23, 2021

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   @JunMa11 Yes, your comments are correct.