<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>

CC BY-SA 4.0
2020-12-11T09:53:13.743Z
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 :)
Cowboy_Patrick
CC BY-SA 4.0
2020-12-12T10:40:17.507Z
Thank you......
Ayoola Olafenwa
CC BY-SA 4.0
2020-12-11T09:41:49.763Z
2020-12-11T09:41:49.763Z
Ayoola Olafenwa
<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>

<python><single-cell><tensorflow>
Segment cell organelles with pixellib
# 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:

Example segmentation result:

**Tags:** python, single-cell, tensorflow

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## 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......  

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