Answers.Body: <p>I am the creator of PixelLib. PixelLib supports instance segmentation of 80 classes of objects using pre-trained coco model. Cell organelles is not part of the classes supported.
  The only solution is to train your own custom model. PixelLib supports custom training of a model using your own labelled dataset. Read this <a href="https://towardsdatascience.com/custom-instance-segmentation-training-with-7-lines-of-code-ff340851e99b" rel="nofollow noreferrer">article</a> I published on how to train a custom model with PixelLib.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-12-11T09:53:13.743Z
Answers.Text: Wow, what an honor that you personally respond. Thank you very much. I actually already found this article and I wanted to avoid labelling the cells by hand. But I guess it pays off in the long run hopefully. I see a lot of potential in your work for Bioinformatics in general and especially for the project i'm working on now. If done right I'm sure PixelLib can help save us a lot of work :)
Answers.UserDisplayName: Cowboy_Patrick
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-12-12T10:40:17.507Z
Answers.Text: Thank you......
Answers.UserDisplayName: Ayoola Olafenwa
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-12-11T09:41:49.763Z
Answers.LastActivityDate: 2020-12-11T09:41:49.763Z
Answers.UserDisplayName: Ayoola Olafenwa
Body: <p>I've got some images of cell organelles and I really want to avoid labeling them by hand. The images all look something like the image below. Is there already an existing model specifially for cells? I tried it with existing models like <code>mask_rcnn_coco.h5</code> or <code>deeplabv3_xception65_ade20k.h5</code> but as they aren't built for cells the results aren't good.</p>
  <p>My code is pretty basic right now:</p>
  <pre><code>from pixellib.instance import instance_segmentation
  
  from PIL import Image
  
      def segmentImage():
          segment_image = instance_segmentation()
      
          segment_image.load_model(&quot;mask_rcnn_coco.h5&quot;)
          segment_image.segmentImage(&quot;cell.jpg&quot;, output_image_name=&quot;cell_seg.jpg&quot;, show_bboxes=True)
  </code></pre>
  <p>Cell example image:</p>
  <p><a href="https://i.sstatic.net/zr3LZ.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/zr3LZ.png" alt="cell organelle example image" /></a></p>
  <p>Example segmentation result:</p>
  <p><a href="https://i.sstatic.net/jkXGw.jpg" rel="nofollow noreferrer"><img src="https://i.sstatic.net/jkXGw.jpg" alt="enter image description here" /></a></p>
  
Tags: <python><single-cell><tensorflow>
Title: Segment cell organelles with pixellib
ThreadText: # Segment cell organelles with pixellib
  
  (Asked by: username_0 on 2020-12-10)
  
  I've got some images of cell organelles and I really want to avoid labeling them by hand. The images all look something like the image below. Is there already an existing model specifially for cells? I tried it with existing models like `mask_rcnn_coco.h5` or `deeplabv3_xception65_ade20k.h5` but as they aren't built for cells the results aren't good.
  
  My code is pretty basic right now:
  
  ```
  from pixellib.instance import instance_segmentation
  
  from PIL import Image
  
      def segmentImage():
          segment_image = instance_segmentation()
      
          segment_image.load_model("mask_rcnn_coco.h5")
          segment_image.segmentImage("cell.jpg", output_image_name="cell_seg.jpg", show_bboxes=True)
  
  ```
  
  Cell example image:
  
  [![cell organelle example image](https://i.sstatic.net/zr3LZ.png)](https://i.sstatic.net/zr3LZ.png)
  
  Example segmentation result:
  
  [![enter image description here](https://i.sstatic.net/jkXGw.jpg)](https://i.sstatic.net/jkXGw.jpg)
  
  **Tags:** python, single-cell, tensorflow
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-12-11. Score: 2 | Accepted answer
  
  I am the creator of PixelLib. PixelLib supports instance segmentation of 80 classes of objects using pre-trained coco model. Cell organelles is not part of the classes supported.
  The only solution is to train your own custom model. PixelLib supports custom training of a model using your own labelled dataset. Read this [article](https://towardsdatascience.com/custom-instance-segmentation-training-with-7-lines-of-code-ff340851e99b) I published on how to train a custom model with PixelLib.
  
  > **Comments:**
  > **username_0**: Wow, what an honor that you personally respond. Thank you very much. I actually already found this article and I wanted to avoid labelling the cells by hand. But I guess it pays off in the long run hopefully. I see a lot of potential in your work for Bioinformatics in general and especially for the project i'm working on now. If done right I'm sure PixelLib can help save us a lot of work :)  
  > **username_1**: Thank you......  
  
  ---
  
  