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Hello Freesurfer experts
I want to apply deep learning to structural brain images processed by Recon-all. My goal is to demonstrate the morphological variation(e.g., gray matter volumes or cortical thickness) of brain regions in structural magnetic resonance imaging using convolutional neural networks (CNN). Then interpret those models on the basis of the region of interest.
But I'm still distracted by the model input . I searched before but still couldn't decide which Recon-all output I should use to perform the task. I think I'll use the folder(mri) but I'm not sure which file.
can any one guide me .
Thanks,
Sorry, can you explain a bit more about what you want to do. What does “demonstrate morphological variation” mean? Just quantify variability? Are there any variables that you are correlating it with (e.g. disease status, cognitive measure, etc…)? I don’t understand what you are trying to do Cheers Bruce
From: freesurfer-bounces@nmr.mgh.harvard.edu freesurfer-bounces@nmr.mgh.harvard.edu On Behalf Of REEM ABU BAKR BAHATHIQ Sent: Thursday, February 24, 2022 7:37 AM To: Freesurfer support list freesurfer@nmr.mgh.harvard.edu Subject: [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution Hello Freesurfer experts
I want to apply deep learning to structural brain images processed by Recon-all. My goal is to demonstrate the morphological variation(e.g., gray matter volumes or cortical thickness) of brain regions in structural magnetic resonance imaging using convolutional neural networks (CNN). Then interpret those models on the basis of the region of interest.
But I'm still distracted by the model input . I searched before but still couldn't decide which Recon-all output I should use to perform the task. I think I'll use the folder(mri) but I'm not sure which file.
can any one guide me .
Thanks,
External Email - Use Caution
Sorry for not making it clear you are right. Yes, I want to diagnose autism spectrum disorder from brain images using structural measurements such as the gray matter volumes of regions or at the voxel level. Then, since deep learning is a black box that simply cannot know what it has learned, there is a so-called "unit of attention" that can point to the most important areas involved in the decision-making of the deep learning model.
في الخميس، 24 فبراير 2022 في 6:19 م تمت كتابة ما يلي بواسطة Fischl, Bruce <BFISCHL@mgh.harvard.edu>:
Sorry, can you explain a bit more about what you want to do. What does “demonstrate morphological variation” mean? Just quantify variability? Are there any variables that you are correlating it with (e.g. disease status, cognitive measure, etc…)? I don’t understand what you are trying to do
Cheers
Bruce
*From:* freesurfer-bounces@nmr.mgh.harvard.edu < freesurfer-bounces@nmr.mgh.harvard.edu> *On Behalf Of *REEM ABU BAKR BAHATHIQ *Sent:* Thursday, February 24, 2022 7:37 AM *To:* Freesurfer support list freesurfer@nmr.mgh.harvard.edu *Subject:* [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution *Hello Freesurfer experts
I want to apply deep learning to structural brain images processed by Recon-all. My goal is to demonstrate the morphological variation(e.g., gray matter volumes or cortical thickness) of brain regions in structural magnetic resonance imaging using convolutional neural networks (CNN). Then interpret those models on the basis of the region of interest.
But I'm still distracted by the model input . I searched before but still couldn't decide which Recon-all output I should use to perform the task. I think I'll use the folder(mri) but I'm not sure which file.
can any one guide me .
Thanks, _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://secure-web.cisco.com/1Nhqb_f9HCF_LDzs7mzo8KHO6tP_hOJ_kYWyHgY-7Y-hBhn...
I see. Well, you will probably want some combination of cortical thickness maps and subcortical volumes at the least, although of course other information might be important as well (connectivity, myelin content, etc…). These things have different shapes (in the python sense) so you’ll have to sort out a network architecture that lets you combine them
Cheers Bruce
From: freesurfer-bounces@nmr.mgh.harvard.edu freesurfer-bounces@nmr.mgh.harvard.edu On Behalf Of REEM ABU BAKR BAHATHIQ Sent: Thursday, February 24, 2022 11:28 AM To: Freesurfer support list freesurfer@nmr.mgh.harvard.edu Subject: Re: [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution Sorry for not making it clear you are right. Yes, I want to diagnose autism spectrum disorder from brain images using structural measurements such as the gray matter volumes of regions or at the voxel level. Then, since deep learning is a black box that simply cannot know what it has learned, there is a so-called "unit of attention" that can point to the most important areas involved in the decision-making of the deep learning model.
في الخميس، 24 فبراير 2022 في 6:19 م تمت كتابة ما يلي بواسطة Fischl, Bruce <BFISCHL@mgh.harvard.edumailto:BFISCHL@mgh.harvard.edu>: Sorry, can you explain a bit more about what you want to do. What does “demonstrate morphological variation” mean? Just quantify variability? Are there any variables that you are correlating it with (e.g. disease status, cognitive measure, etc…)? I don’t understand what you are trying to do Cheers Bruce
From: freesurfer-bounces@nmr.mgh.harvard.edumailto:freesurfer-bounces@nmr.mgh.harvard.edu <freesurfer-bounces@nmr.mgh.harvard.edumailto:freesurfer-bounces@nmr.mgh.harvard.edu> On Behalf Of REEM ABU BAKR BAHATHIQ Sent: Thursday, February 24, 2022 7:37 AM To: Freesurfer support list <freesurfer@nmr.mgh.harvard.edumailto:freesurfer@nmr.mgh.harvard.edu> Subject: [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution Hello Freesurfer experts
I want to apply deep learning to structural brain images processed by Recon-all. My goal is to demonstrate the morphological variation(e.g., gray matter volumes or cortical thickness) of brain regions in structural magnetic resonance imaging using convolutional neural networks (CNN). Then interpret those models on the basis of the region of interest.
But I'm still distracted by the model input . I searched before but still couldn't decide which Recon-all output I should use to perform the task. I think I'll use the folder(mri) but I'm not sure which file.
can any one guide me .
Thanks, _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edumailto:Freesurfer@nmr.mgh.harvard.edu MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurferhttps://secure-web.cisco.com/1Nhqb_f9HCF_LDzs7mzo8KHO6tP_hOJ_kYWyHgY-7Y-hBhnv1q-tSFjqkdsvtL6uOVeithziUno3bDr3SWwXV0CneXrPaChtC5G24GCuCfeLhCxl0yf24Hr_lzN21IY34PHJMFjGPGijxBlXX3Dem0QtRMo2-XjMu16CnhhNEZTIUzlOyfHmOUkDjyicH1GoqUG1NPwhROefuYCZsNv9XtAbkexx7yxEuk409LmiPwasjYpkbSmiFkDrc70CJPFWJXxV5oDxhjAX_dpX4ofL2QDHHXGjJFI8PRL2fBaGQb8alfhTJ-h7Xi6QQkd_sKzT83EJBAIZJffsnmDZyzGV_Hw/https%3A%2F%2Fmail.nmr.mgh.harvard.edu%2Fmailman%2Flistinfo%2Ffreesurfer
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great ..
I want the sizes of the sub-cortical regions, will I depend here on the output of the mri folder, for example the image wmparc .mgz, or do I need another image or other data?
Is there a reference that can help me extract image features for deep learning?
Thanks in advance
في الخميس، 24 فبراير 2022 في 7:35 م تمت كتابة ما يلي بواسطة Fischl, Bruce <BFISCHL@mgh.harvard.edu>:
I see. Well, you will probably want some combination of cortical thickness maps and subcortical volumes at the least, although of course other information might be important as well (connectivity, myelin content, etc…). These things have different shapes (in the python sense) so you’ll have to sort out a network architecture that lets you combine them
Cheers
Bruce
*From:* freesurfer-bounces@nmr.mgh.harvard.edu < freesurfer-bounces@nmr.mgh.harvard.edu> *On Behalf Of *REEM ABU BAKR BAHATHIQ *Sent:* Thursday, February 24, 2022 11:28 AM *To:* Freesurfer support list freesurfer@nmr.mgh.harvard.edu *Subject:* Re: [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution *Sorry for not making it clear
you are right.
Yes, I want to diagnose autism spectrum disorder from brain images using structural measurements such as the gray matter volumes of regions or at the voxel level. Then, since deep learning is a black box that simply cannot know what it has learned, there is a so-called "unit of attention" that can point to the most important areas involved in the decision-making of the deep learning model.
في الخميس، 24 فبراير 2022 في 6:19 م تمت كتابة ما يلي بواسطة Fischl, Bruce <BFISCHL@mgh.harvard.edu>:
Sorry, can you explain a bit more about what you want to do. What does “demonstrate morphological variation” mean? Just quantify variability? Are there any variables that you are correlating it with (e.g. disease status, cognitive measure, etc…)? I don’t understand what you are trying to do
Cheers
Bruce
*From:* freesurfer-bounces@nmr.mgh.harvard.edu < freesurfer-bounces@nmr.mgh.harvard.edu> *On Behalf Of *REEM ABU BAKR BAHATHIQ *Sent:* Thursday, February 24, 2022 7:37 AM *To:* Freesurfer support list freesurfer@nmr.mgh.harvard.edu *Subject:* [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution *Hello Freesurfer experts
I want to apply deep learning to structural brain images processed by Recon-all. My goal is to demonstrate the morphological variation(e.g., gray matter volumes or cortical thickness) of brain regions in structural magnetic resonance imaging using convolutional neural networks (CNN). Then interpret those models on the basis of the region of interest.
But I'm still distracted by the model input . I searched before but still couldn't decide which Recon-all output I should use to perform the task. I think I'll use the folder(mri) but I'm not sure which file.
can any one guide me .
Thanks,
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu *MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be* https://secure-web.cisco.com/1CiNr9-ZbORfNwKb1W0N4O_aKMrL58JWVGaLfZmzYohXT5Z... https://secure-web.cisco.com/1Nhqb_f9HCF_LDzs7mzo8KHO6tP_hOJ_kYWyHgY-7Y-hBhnv1q-tSFjqkdsvtL6uOVeithziUno3bDr3SWwXV0CneXrPaChtC5G24GCuCfeLhCxl0yf24Hr_lzN21IY34PHJMFjGPGijxBlXX3Dem0QtRMo2-XjMu16CnhhNEZTIUzlOyfHmOUkDjyicH1GoqUG1NPwhROefuYCZsNv9XtAbkexx7yxEuk409LmiPwasjYpkbSmiFkDrc70CJPFWJXxV5oDxhjAX_dpX4ofL2QDHHXGjJFI8PRL2fBaGQb8alfhTJ-h7Xi6QQkd_sKzT83EJBAIZJffsnmDZyzGV_Hw/https%3A%2F%2Fmail.nmr.mgh.harvard.edu%2Fmailman%2Flistinfo%2Ffreesurfer
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I have a python module that extracts everything I considered useful from a full recon-all output folder for machine learning. I actually used it for predicting ASD.
Have a look here: https://secure-web.cisco.com/1y2aCFTmfPbByJKeWu1_Uh_jIlFUNKJ_d_VL40iCrt_69Bx...
Best,
Tim
On 03/10/2022 4:06 PM REEM ABU BAKR BAHATHIQ rbahathiq0001@stu.kau.edu.sa wrote:
External Email - Use Caution
great ..
I want the sizes of the sub-cortical regions, will I depend here on the output of the mri folder, for example the image wmparc .mgz, or do I need another image or other data?
Is there a reference that can help me extract image features for deep learning?
Thanks in advance
في الخميس، 24 فبراير 2022 في 7:35 م تمت كتابة ما يلي بواسطة Fischl, Bruce <BFISCHL@mgh.harvard.edu>:
I see. Well, you will probably want some combination of cortical thickness maps and subcortical volumes at the least, although of course other information might be important as well (connectivity, myelin content, etc…). These things have different shapes (in the python sense) so you’ll have to sort out a network architecture that lets you combine them
Cheers
Bruce
*From:* freesurfer-bounces@nmr.mgh.harvard.edu < freesurfer-bounces@nmr.mgh.harvard.edu> *On Behalf Of *REEM ABU BAKR BAHATHIQ *Sent:* Thursday, February 24, 2022 11:28 AM *To:* Freesurfer support list freesurfer@nmr.mgh.harvard.edu *Subject:* Re: [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution *Sorry for not making it clear
you are right.
Yes, I want to diagnose autism spectrum disorder from brain images using structural measurements such as the gray matter volumes of regions or at the voxel level. Then, since deep learning is a black box that simply cannot know what it has learned, there is a so-called "unit of attention" that can point to the most important areas involved in the decision-making of the deep learning model.
في الخميس، 24 فبراير 2022 في 6:19 م تمت كتابة ما يلي بواسطة Fischl, Bruce <BFISCHL@mgh.harvard.edu>:
Sorry, can you explain a bit more about what you want to do. What does “demonstrate morphological variation” mean? Just quantify variability? Are there any variables that you are correlating it with (e.g. disease status, cognitive measure, etc…)? I don’t understand what you are trying to do
Cheers
Bruce
*From:* freesurfer-bounces@nmr.mgh.harvard.edu < freesurfer-bounces@nmr.mgh.harvard.edu> *On Behalf Of *REEM ABU BAKR BAHATHIQ *Sent:* Thursday, February 24, 2022 7:37 AM *To:* Freesurfer support list freesurfer@nmr.mgh.harvard.edu *Subject:* [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution *Hello Freesurfer experts
I want to apply deep learning to structural brain images processed by Recon-all. My goal is to demonstrate the morphological variation(e.g., gray matter volumes or cortical thickness) of brain regions in structural magnetic resonance imaging using convolutional neural networks (CNN). Then interpret those models on the basis of the region of interest.
But I'm still distracted by the model input . I searched before but still couldn't decide which Recon-all output I should use to perform the task. I think I'll use the folder(mri) but I'm not sure which file.
can any one guide me .
Thanks,
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu *MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be* https://secure-web.cisco.com/1CiNr9-ZbORfNwKb1W0N4O_aKMrL58JWVGaLfZmzYohXT5Z... https://secure-web.cisco.com/1Nhqb_f9HCF_LDzs7mzo8KHO6tP_hOJ_kYWyHgY-7Y-hBhnv1q-tSFjqkdsvtL6uOVeithziUno3bDr3SWwXV0CneXrPaChtC5G24GCuCfeLhCxl0yf24Hr_lzN21IY34PHJMFjGPGijxBlXX3Dem0QtRMo2-XjMu16CnhhNEZTIUzlOyfHmOUkDjyicH1GoqUG1NPwhROefuYCZsNv9XtAbkexx7yxEuk409LmiPwasjYpkbSmiFkDrc70CJPFWJXxV5oDxhjAX_dpX4ofL2QDHHXGjJFI8PRL2fBaGQb8alfhTJ-h7Xi6QQkd_sKzT83EJBAIZJffsnmDZyzGV_Hw/https%3A%2F%2Fmail.nmr.mgh.harvard.edu%2Fmailman%2Flistinfo%2Ffreesurfer
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Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://secure-web.cisco.com/1_sOxWq-OQDWEOxPcEKBn9q1GB5rQ4sZ4mSunp7W97gJbQH... The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Mass General Brigham Compliance HelpLine at https://secure-web.cisco.com/1xqflSoiP80jlR25KFtboT6lNStxvdTyLiMcgJ3cqhpAxLc... https://secure-web.cisco.com/1xqflSoiP80jlR25KFtboT6lNStxvdTyLiMcgJ3cqhpAxLcVDFcgdSMGE76l3uFlmkwPtexoPAcbfbU7nIONE8jJgVeemF1qCAXoOo3a2bDINqxBy7Ac-7-QRNvBm-ncpdJ9wmeCY9LLFebjSjdmBq3mrNT1qn7UDTl7Ktxlo9_g4-Z1V9U0G7HBJ6ivnqwOBba-XZbEdAKrPQju48rNj587SZkXy9-GXKVNCaiU3jv8OIRRdS7-KyYBMlpEc3M9IEJIf78Lwyq1MVUMlf0P3FTlt56MHpGy0Wgdzu0fOqekj-qruC1HQ6eCQChaB_han/https%3A%2F%2Fwww.massgeneralbrigham.org%2Fcomplianceline . Please note that this e-mail is not secure (encrypted). If you do not wish to continue communication over unencrypted e-mail, please notify the sender of this message immediately. Continuing to send or respond to e-mail after receiving this message means you understand and accept this risk and wish to continue to communicate over unencrypted e-mail.
-- Dr. Tim Schäfer Postdoc Computational Neuroimaging Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
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Thank you so much Can I access your codes applicable to ASD
في الخميس، 10 مارس 2022 في 7:02 م تمت كتابة ما يلي بواسطة Tim Schäfer < ts+ml@rcmd.org>:
External Email - Use CautionI have a python module that extracts everything I considered useful from a full recon-all output folder for machine learning. I actually used it for predicting ASD.
Have a look here: https://secure-web.cisco.com/1y2aCFTmfPbByJKeWu1_Uh_jIlFUNKJ_d_VL40iCrt_69Bx...
Best,
Tim
On 03/10/2022 4:06 PM REEM ABU BAKR BAHATHIQ <
rbahathiq0001@stu.kau.edu.sa> wrote:
External Email - Use Caution
great ..
I want the sizes of the sub-cortical regions, will I depend here on the output of the mri folder, for example the image wmparc .mgz, or do I need another image or other data?
Is there a reference that can help me extract image features for deep learning?
Thanks in advance
في الخميس، 24 فبراير 2022 في 7:35 م تمت كتابة ما يلي بواسطة Fischl, Bruce <BFISCHL@mgh.harvard.edu>:
I see. Well, you will probably want some combination of cortical
thickness
maps and subcortical volumes at the least, although of course other information might be important as well (connectivity, myelin content, etc…). These things have different shapes (in the python sense) so
you’ll
have to sort out a network architecture that lets you combine them
Cheers
Bruce
*From:* freesurfer-bounces@nmr.mgh.harvard.edu < freesurfer-bounces@nmr.mgh.harvard.edu> *On Behalf Of *REEM ABU BAKR BAHATHIQ *Sent:* Thursday, February 24, 2022 11:28 AM *To:* Freesurfer support list freesurfer@nmr.mgh.harvard.edu *Subject:* Re: [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution *Sorry for not making it clear
you are right.
Yes, I want to diagnose autism spectrum disorder from brain images
using
structural measurements such as the gray matter volumes of regions or
at
the voxel level. Then, since deep learning is a black box that simply cannot know what
it
has learned, there is a so-called "unit of attention" that can point
to the
most important areas involved in the decision-making of the deep
learning
model.
في الخميس، 24 فبراير 2022 في 6:19 م تمت كتابة ما يلي بواسطة Fischl, Bruce <BFISCHL@mgh.harvard.edu>:
Sorry, can you explain a bit more about what you want to do. What does “demonstrate morphological variation” mean? Just quantify variability?
Are
there any variables that you are correlating it with (e.g. disease
status,
cognitive measure, etc…)? I don’t understand what you are trying to do
Cheers
Bruce
*From:* freesurfer-bounces@nmr.mgh.harvard.edu < freesurfer-bounces@nmr.mgh.harvard.edu> *On Behalf Of *REEM ABU BAKR BAHATHIQ *Sent:* Thursday, February 24, 2022 7:37 AM *To:* Freesurfer support list freesurfer@nmr.mgh.harvard.edu *Subject:* [Freesurfer] recon-all outpputs for deep learning
External Email - Use Caution *Hello Freesurfer experts
I want to apply deep learning to structural brain images processed by Recon-all. My goal is to demonstrate the morphological variation(e.g.,
gray
matter volumes or cortical thickness) of brain regions in structural magnetic resonance imaging using convolutional neural networks (CNN).
Then
interpret those models on the basis of the region of interest.
But I'm still distracted by the model input . I searched before but
still
couldn't decide which Recon-all output I should use to perform the
task. I
think I'll use the folder(mri) but I'm not sure which file.
can any one guide me .
Thanks,
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu *MailScanner has detected a possible fraud attempt from "secure-web.cisco.com" claiming to be*
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-- Dr. Tim Schäfer Postdoc Computational Neuroimaging Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy University Hospital Frankfurt, Goethe University Frankfurt am Main, Germany
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://secure-web.cisco.com/11G-zgSlQ7K63o_ZwscjM_GvAQHX5hNX-dUxlRkbV_Tyx_C... The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Mass General Brigham Compliance HelpLine at https://secure-web.cisco.com/18rbBTo1Dr_3OYTH-kpjPe1aHAXvWqRZ-Eqbt9kdixGMfQO... < https://secure-web.cisco.com/18rbBTo1Dr_3OYTH-kpjPe1aHAXvWqRZ-Eqbt9kdixGMfQO... . Please note that this e-mail is not secure (encrypted). If you do not wish to continue communication over unencrypted e-mail, please notify the sender of this message immediately. Continuing to send or respond to e-mail after receiving this message means you understand and accept this risk and wish to continue to communicate over unencrypted e-mail.
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REEM ABU BAKR BAHATHIQ -
Tim Schäfer