Color scheme for color blind people?

[gopher-confocal]

I did a very detailed cell segmentation benchmarking of various open-source software with version 1.0.6 of the MiC plugin and it worked wonderfully. One of the reviewers for Scientific Reports asked for a color scheme to accommodate color-blind people. Is it possible to develop in the plugin an option for a different color scheme for color-blind people? Thank you for your help.

[Multimodal-Imaging-Center]

Hi,

using version 1.0.6 you have two kind of output images.

• version 2D : composite of 4 images (Red, green, blue and gray) you may change the LookUp Table (LUT) for each image directly in ImageJ/Fiji. However, you will have to reinterpret the combined color in comparison to the ones I explained.
• version 3D : each type of interaction (TP,FN, FP, over/under estimation, fuse, split....) is given a specific value and I apply a LUT. you can change the LUT as you want to accomadate for colorblind people.

starting with version 1.0.7, I unified the output of the 2D version with the 3D version.

If you have recommandations of the review or a color-blind person that can help me to provide a better LUT, I will adapt it and update the plugin in consequence.

Best,

[gopher-confocal]

For the pixel-based analysis, I used a color scheme that used magenta for red, cyan for green, and white for overlap. The object-based analysis is much more problematic and in our manuscript we used your original colors. If a color-blind option were available in the GUI for object-based analysis, perhaps pink or magenta could be used for the red color and brown for the green color? It's hard to select with so many colors needed. Thank you for considering this. *UIC Image Analysis Office Hours:* Mondays 9 - 10:30 AM, Jackson Hall, rm. 1-153 Mondays 2 - 3:30 PM, Snyder Hall, rm. 23 Fridays 9 - 10:30 AM, CCRB, rm. 1-220 **Any work utilizing UIC equipment or staff support must acknowledge the University Imaging Centers in publications and presentations. SCR 020997**

…

________________________________________________ Mary E. Brown, Pharm.D. Imaging Specialist/Research Professional 3/Lead Image Data Analyst University Imaging Centers Snyder Hall, rm. 23F 1475 Gortner Avenue Saint Paul, MN 55108 Email: ***@***.*** Phone: 773-458-3090

On Mon, Aug 17, 2026 at 7:57 AM Multimodal-Imaging-Center < ***@***.***> wrote: *Multimodal-Imaging-Center* left a comment (MultimodalImagingCenter/MiC#7) <#7 (comment)> Hi, using version 1.0.6 you have two kind of output images. - version 2D : composite of 4 images (Red, green, blue and gray) you may change the LookUp Table (LUT) for each image directly in ImageJ/Fiji. However, you will have to reinterpret the combined color in comparison to the ones I explained. - version 3D : each type of interaction (TP,FN, FP, over/under estimation, fuse, split....) is given a specific value and I apply a LUT. you can change the LUT as you want to accomadate for colorblind people. starting with version 1.0.7, I unified the output of the 2D version with the 3D version. If you have recommandations of the review or a color-blind person that can help me to provide a better LUT, I will adapt it and update the plugin in consequence. Best, — Reply to this email directly, view it on GitHub <#7?email_source=notifications&email_token=AMB72WHZCA4VIQ245PVZ7FT5KL6K7A5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZRGYZTINZWGEZKM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KYZTPN52GK4S7MNWGSY3L#issuecomment-5316347612>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AMB72WGDJIFQPQ7CLYSET7T5KL6K7AVCNFSNUABFKJSXA33TNF2G64TZHM2TSNRRGMZTANRTHNEXG43VMU5TIOJZHA4TMMRVHAY2C5QC> . Triage notifications, keep track of coding agent tasks and review pull requests on the go with GitHub Mobile for iOS <https://github.com/notifications/mobile/ios/AMB72WFKBGS6YGXI6LUMNYT5KL6K7A5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZRGYZTINZWGEZKM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KUZTPN52GK4S7NFXXG> and Android <https://github.com/notifications/mobile/android/AMB72WCKRO4SPLFXFOSC5OD5KL6K7A5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZRGYZTINZWGEZKM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2K4ZTPN52GK4S7MFXGI4TPNFSA>. Download it today! You are receiving this because you authored the thread.Message ID: ***@***.***>

[Multimodal-Imaging-Center]

I have done a test with : Channel 1 : magenta Channel 2 : cyan Channel 3 : blue I then changed the displayed value using brightness/contrast tool. With the "Set" button I defined the maximum to 300 and checked the "propagate to the other 3 channels of this image" checkbox. (This step can be replaced by multiplying masks by a factor between 0 and 1) Please try and tell me if it better for color-blind people To show you the effect, this result image : [cid:c8f1055a-69cd-409d-b583-9f5b51f19dd9] Becomes [cid:85a8958d-8a82-4a5f-b478-72c08c0327fb]

________________________________ De: GopherConfocal ***@***.***> Envoyé: Lundi 17 août 2026 23:21 À: MultimodalImagingCenter/MiC ***@***.***> Cc: mic.ipa ***@***.***>; Comment ***@***.***> Objet: Re: [MultimodalImagingCenter/MiC] Color scheme for color blind people? (Issue #7) [https://outlook.office.com/mail/inbox/id/AAkALgAAAAAAHYQDEapmEc2byACqAC%2FEWg0AIXwZJnY57EWO%2FH3o%2F1xRRAAFzfIHEwAA?nativeVersion=1.2026.730.100]gopher-confocal left a comment (MultimodalImagingCenter/MiC#7)<#7 (comment)> For the pixel-based analysis, I used a color scheme that used magenta for red, cyan for green, and white for overlap. The object-based analysis is much more problematic and in our manuscript we used your original colors. If a color-blind option were available in the GUI for object-based analysis, perhaps pink or magenta could be used for the red color and brown for the green color? It's hard to select with so many colors needed. Thank you for considering this. *UIC Image Analysis Office Hours:* Mondays 9 - 10:30 AM, Jackson Hall, rm. 1-153 Mondays 2 - 3:30 PM, Snyder Hall, rm. 23 Fridays 9 - 10:30 AM, CCRB, rm. 1-220 **Any work utilizing UIC equipment or staff support must acknowledge the University Imaging Centers in publications and presentations. SCR 020997**

________________________________________________ Mary E. Brown, Pharm.D. Imaging Specialist/Research Professional 3/Lead Image Data Analyst University Imaging Centers Snyder Hall, rm. 23F 1475 Gortner Avenue Saint Paul, MN 55108 Email: ***@***.*** Phone: 773-458-3090

On Mon, Aug 17, 2026 at 7:57 AM Multimodal-Imaging-Center < ***@***.***> wrote: *Multimodal-Imaging-Center* left a comment (MultimodalImagingCenter/MiC#7) <#7 (comment)> Hi, using version 1.0.6 you have two kind of output images. - version 2D : composite of 4 images (Red, green, blue and gray) you may change the LookUp Table (LUT) for each image directly in ImageJ/Fiji. However, you will have to reinterpret the combined color in comparison to the ones I explained. - version 3D : each type of interaction (TP,FN, FP, over/under estimation, fuse, split....) is given a specific value and I apply a LUT. you can change the LUT as you want to accomadate for colorblind people. starting with version 1.0.7, I unified the output of the 2D version with the 3D version. If you have recommandations of the review or a color-blind person that can help me to provide a better LUT, I will adapt it and update the plugin in consequence. Best, — Reply to this email directly, view it on GitHub <#7?email_source=notifications&email_token=AMB72WHZCA4VIQ245PVZ7FT5KL6K7A5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZRGYZTINZWGEZKM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KYZTPN52GK4S7MNWGSY3L#issuecomment-5316347612>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AMB72WGDJIFQPQ7CLYSET7T5KL6K7AVCNFSNUABFKJSXA33TNF2G64TZHM2TSNRRGMZTANRTHNEXG43VMU5TIOJZHA4TMMRVHAY2C5QC> . Triage notifications, keep track of coding agent tasks and review pull requests on the go with GitHub Mobile for iOS <https://github.com/notifications/mobile/ios/AMB72WFKBGS6YGXI6LUMNYT5KL6K7A5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZRGYZTINZWGEZKM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KUZTPN52GK4S7NFXXG> and Android <https://github.com/notifications/mobile/android/AMB72WCKRO4SPLFXFOSC5OD5KL6K7A5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZRGYZTINZWGEZKM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2K4ZTPN52GK4S7MFXGI4TPNFSA>. Download it today! You are receiving this because you authored the thread.Message ID: ***@***.***>

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[gopher-confocal]

Thank you for your efforts. I'm sorry, but I cannot open the images. I put the link in my Google address bar and I got this error:

[cmessaoudi]

sorry,

here are the images
before :

after :

is it better ?

[gopher-confocal]

Is this for pixel-based analysis or object-based analysis or both? Does channel 1 represent the ground-truth object pixels and channel 2 represent the test mask pixels? What does channel 3 represent? Thank you for your clarification. Your new color scheme would help color-blind people. Previously I had suggested brown substituting for the green color in object-based analysis, but would not be very appealing to the eye. Maybe use dark gray for pixels that are not counted on the image border and light gray for the green color and magenta or pink for red in object-based analysis? I really appreciate your consideration! *UIC Image Analysis Office Hours:* Mondays 9 - 10:30 AM, Jackson Hall, rm. 1-153 Mondays 2 - 3:30 PM, Snyder Hall, rm. 23 Fridays 9 - 10:30 AM, CCRB, rm. 1-220 **Any work utilizing UIC equipment or staff support must acknowledge the University Imaging Centers in publications and presentations. SCR 020997**

…

________________________________________________ Mary E. Brown, Pharm.D. Imaging Specialist/Research Professional 3/Lead Image Data Analyst University Imaging Centers Snyder Hall, rm. 23F 1475 Gortner Avenue Saint Paul, MN 55108 Email: ***@***.*** Phone: 773-458-3090

On Tue, Aug 18, 2026 at 10:21 AM Cédric MESSAOUDI ***@***.***> wrote: *cmessaoudi* left a comment (MultimodalImagingCenter/MiC#7) <#7 (comment)> sorry, here are the images before : Image: Image (view on web) <https://github.com/user-attachments/assets/ce1d9b16-4b2a-47a5-9f82-7ff86d69eb38> after : Image: Image (view on web) <https://github.com/user-attachments/assets/b8d2a2a4-493f-4a35-b09c-8ca526c3b1d9> is it better ? — Reply to this email directly, view it on GitHub <#7?email_source=notifications&email_token=AMB72WFBALC4LHRJMMB6O2D5KRX5RA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTGAZDQMBTGU3KM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KYZTPN52GK4S7MNWGSY3L#issuecomment-5330280356>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AMB72WF2KH2IAAHSXB3Z7AL5KRX5RAVCNFSNUABFKJSXA33TNF2G64TZHM2TSNRRGMZTANRTHNEXG43VMU5TIOJZHA4TMMRVHAY2C5QC> . Triage notifications, keep track of coding agent tasks and review pull requests on the go with GitHub Mobile for iOS <https://github.com/notifications/mobile/ios/AMB72WC6ZTKECC5IDI4EDUL5KRX5RA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTGAZDQMBTGU3KM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KUZTPN52GK4S7NFXXG> and Android <https://github.com/notifications/mobile/android/AMB72WG2SIQPIWVOEOTKVND5KRX5RA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTGAZDQMBTGU3KM4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2K4ZTPN52GK4S7MFXGI4TPNFSA>. Download it today! You are receiving this because you authored the thread.Message ID: ***@***.***>

[cmessaoudi]

this image is for object-based analysis.
1rst channel correspond to objects in ground truth (value 255 for the ones with a correspondance -TP- found, 128 for the ones with no correspondance - FN)
2nd channel corresponds to objects in test mask with a correspondance found in ground truth (TP)
The 3rd channel corresponds to objects in test mask with NO correspondance found in ground truth (FP) or with a correspondance with a IoU score lower than the IoU threshold (and thus considered as FP).

the colors are created automatically by combining the colors of the 3 channels described

with the actual version (green, red, blue) the combination gives:
yellow (TP): these are objects common to both masks (GT, test) and validated by the given IoU threshold.
red (TP): these are true positives with objects that are overestimated (larger in the test mask).
green (TP): these are true positives with objects that are underestimated (smaller in the test mask).
orange (FN): These are false negatives corresponding to the merging (fusion) of objects in the GT mask into a single object in the test mask, one of whose objects is validated by the given IoU threshold. One of the objects in the GT mask will be yellow, while the others will be orange.
cyan (FP): These are False positives when an object in the GT mask is split into multiple objects in the test mask. One object in the test mask will be yellow or cyan (depending on IoU validation), while others will be cyan. or it can be corresponding objects with a IoU lower than the threshold
dark green (FN): these are false negatives, objects in the GT mask that don't match any object in test mask.
grey: these are objects eliminated with respect of distance to border parameter.

I won't be able to work on this today, I will try to find a solution tomorrow.
Best,

[gopher-confocal]

Thank you for the clarification and I appreciate your efforts tremendously! *UIC Image Analysis Office Hours:* Mondays 9 - 10:30 AM, Jackson Hall, rm. 1-153 Mondays 2 - 3:30 PM, Snyder Hall, rm. 23 Fridays 9 - 10:30 AM, CCRB, rm. 1-220 **Any work utilizing UIC equipment or staff support must acknowledge the University Imaging Centers in publications and presentations. SCR 020997**

…

________________________________________________ Mary E. Brown, Pharm.D. Imaging Specialist/Research Professional 3/Lead Image Data Analyst University Imaging Centers Snyder Hall, rm. 23F 1475 Gortner Avenue Saint Paul, MN 55108 Email: ***@***.*** Phone: 773-458-3090

On Tue, Aug 18, 2026 at 11:08 AM Cédric MESSAOUDI ***@***.***> wrote: *cmessaoudi* left a comment (MultimodalImagingCenter/MiC#7) <#7 (comment)> this image is for object-based analysis. 1rst channel correspond to objects in ground truth (value 255 for the ones with a correspondance -TP- found, 128 for the ones with no correspondance - FN) 2nd channel corresponds to objects in test mask with a correspondance found in ground truth (TP) The 3rd channel corresponds to objects in test mask with NO correspondance found in ground truth (FP) or with a correspondance with a IoU score lower than the IoU threshold (and thus considered as FP). the colors are created automatically by combining the colors of the 3 channels described with the actual version (green, red, blue) the combination gives: yellow (TP): these are objects common to both masks (GT, test) and validated by the given IoU threshold. red (TP): these are true positives with objects that are overestimated (larger in the test mask). green (TP): these are true positives with objects that are underestimated (smaller in the test mask). orange (FN): These are false negatives corresponding to the merging (fusion) of objects in the GT mask into a single object in the test mask, one of whose objects is validated by the given IoU threshold. One of the objects in the GT mask will be yellow, while the others will be orange. cyan (FP): These are False positives when an object in the GT mask is split into multiple objects in the test mask. One object in the test mask will be yellow or cyan (depending on IoU validation), while others will be cyan. or it can be corresponding objects with a IoU lower than the threshold dark green (FN): these are false negatives, objects in the GT mask that don't match any object in test mask. grey: these are objects eliminated with respect of distance to border parameter. I won't be able to work on this today, I will try to find a solution tomorrow. Best, — Reply to this email directly, view it on GitHub <#7?email_source=notifications&email_token=AMB72WAZ5PBHR2T7A2P57VT5KR5PVA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTGA4DOOBQGE22M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KYZTPN52GK4S7MNWGSY3L#issuecomment-5330878015>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AMB72WGYITKPRCADTLHYZEL5KR5PVAVCNFSNUABFKJSXA33TNF2G64TZHM2TSNRRGMZTANRTHNEXG43VMU5TIOJZHA4TMMRVHAY2C5QC> . Triage notifications, keep track of coding agent tasks and review pull requests on the go with GitHub Mobile for iOS <https://github.com/notifications/mobile/ios/AMB72WCTO2CVJHBSDYT5LGL5KR5PVA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTGA4DOOBQGE22M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KUZTPN52GK4S7NFXXG> and Android <https://github.com/notifications/mobile/android/AMB72WG6VYRQ4R32CKK6VRT5KR5PVA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTGA4DOOBQGE22M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2K4ZTPN52GK4S7MFXGI4TPNFSA>. Download it today! You are receiving this because you authored the thread.Message ID: ***@***.***>

[cmessaoudi]

Hi,
I have tried to find a solution (with the help of copilot to guide me on the problematic of color-blind people)
similarly to yesterday I propose to change the LUT as follow:

• channel 1 : yellow
• channel 2: magenta
• channel 3: cyan
  then using brightnes/contrast tool (button "Set") define the display range to 0-300 and check the box "propagate to the other 3 channels of this image"

you should get this kind of image

with :
light orange/pink : TP
dark yellow : TP overestimated
magenta : TP underestimated
cyan : not found (IoU < threshold) or split
light orange : split
blue-purple : merge

[gopher-confocal]

That looks great, Cedric. I really appreciate your efforts with a color blind option and with the average precision calculations! I'm going to present a workshop at Images2Knowledge on assessing cell instance segmentation accuracy with your FIJI plugin and I will absolutely credit you and sing your praises! *UIC Image Analysis Office Hours:* Mondays 9 - 10:30 AM, Jackson Hall, rm. 1-153 Mondays 2 - 3:30 PM, Snyder Hall, rm. 23 Fridays 9 - 10:30 AM, CCRB, rm. 1-220 **Any work utilizing UIC equipment or staff support must acknowledge the University Imaging Centers in publications and presentations. SCR 020997**

…

________________________________________________ Mary E. Brown, Pharm.D. Imaging Specialist/Research Professional 3/Lead Image Data Analyst University Imaging Centers Snyder Hall, rm. 23F 1475 Gortner Avenue Saint Paul, MN 55108 Email: ***@***.*** Phone: 773-458-3090

On Wed, Aug 19, 2026 at 3:53 AM Cédric MESSAOUDI ***@***.***> wrote: *cmessaoudi* left a comment (MultimodalImagingCenter/MiC#7) <#7 (comment)> Hi, I have tried to find a solution (with the help of copilot to guide me on the problematic of color-blind people) similarly to yesterday I propose to change the LUT as follow: - channel 1 : yellow - channel 2: magenta - channel 3: cyan then using brightnes/contrast tool (button "Set") define the display range to 0-300 and check the box "propagate to the other 3 channels of this image" Image: Image (view on web) <https://github.com/user-attachments/assets/99995b33-e8ea-49e0-a1f5-910c16df6d14> you should get this kind of image Image: Image (view on web) <https://github.com/user-attachments/assets/14d35175-198d-41a2-9377-dd95eddc6859> with : light orange/pink : TP dark yellow : TP overestimated magenta : TP underestimated cyan : not found (IoU < threshold) or split light orange : split blue-purple : merge — Reply to this email directly, view it on GitHub <#7?email_source=notifications&email_token=AMB72WFURNLLM2WW3K2GVGD5KVTHBA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTHE3TQNZTGQ42M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KYZTPN52GK4S7MNWGSY3L#issuecomment-5339787349>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AMB72WHOZCB4O37B3ERT3UL5KVTHBAVCNFSNUABFKJSXA33TNF2G64TZHM2TSNRRGMZTANRTHNEXG43VMU5TIOJZHA4TMMRVHAY2C5QC> . Triage notifications, keep track of coding agent tasks and review pull requests on the go with GitHub Mobile for iOS <https://github.com/notifications/mobile/ios/AMB72WDYKCTNNXYQOPMCTQ35KVTHBA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTHE3TQNZTGQ42M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2KUZTPN52GK4S7NFXXG> and Android <https://github.com/notifications/mobile/android/AMB72WFFLE7ZBFB3WDPQ66T5KVTHBA5CNFSNUABFM5UWIORPF5TWS5BNNB2WEL2JONZXKZKDN5WW2ZLOOQXTKMZTHE3TQNZTGQ42M4TFMFZW63VGMF2XI2DPOKSWK5TFNZ2K4ZTPN52GK4S7MFXGI4TPNFSA>. Download it today! You are receiving this because you authored the thread.Message ID: ***@***.***>