This is a link to Nature which published the article. The accompanying set of video clips is compelling; in one frame, a single nerve projection is traced out among a tangle of other neurons. The technique will provide a boost for the field of connectomics; researchers who would otherwise have to reconstruct neural wiring from small piece-meal sections of brain will be able to view whole-brain connections in one glance.
Tuesday, April 23, 2013
The see-through brain
This is a link to Nature which published the article. The accompanying set of video clips is compelling; in one frame, a single nerve projection is traced out among a tangle of other neurons. The technique will provide a boost for the field of connectomics; researchers who would otherwise have to reconstruct neural wiring from small piece-meal sections of brain will be able to view whole-brain connections in one glance.
Saturday, February 18, 2012
The use of a spatial distribution model in labeling sulci
While accurate sulcal identification can be a challenge even for expert neuroanatomists, there are sulci that are to some degree more consistent, and for which anatomical correspondence can be established across subjects. These are the larger primary sulci. The localization of these sulci allows us to generate a spatial distribution or probabilistic map which can be used to label candidate sulci. A graph that maps the structural relationships between sulci can also be constructed and unlabeled sulci (or the more variable secondary and tertiary sulci) can be identified against this reference.
These two ideas, the use of the probabilistic atlas and the graph, have been incorporated into automated and semi-automated labeling methods in various ways. In this post I will present the basic idea behind the use of the spatial distribution model.
The use of a probabilistic atlas
Probabilistic maps compute the probability for each tissue class at every voxel location using a large database of segmented and labeled anatomical structures. Evans et al. [3] coined the name Statistical Probabilistic Anatomical Maps or SPAM for these models. Paul Thompson has a nice description of these SPAM models and the Brainvisa website has a nice visualization of a sulcal atlas which is reproduced below:
A straightforward implementation of the probabilistic atlas paradigm can be seen in Le Goualher et al. [1] [2]. SPAM models give the probability for each sulcal class so that at any given location, unlabeled sulci are assigned the most probable label for that location. In other words, to label a new sulcus
:
Compute:] "\theta_k = \arg\max_{\theta}[p(\theta_i)]")
where p is the probability from a SPAM atlas
Assign:
The use of a point distribution model
A different spatial distribution model is used by Lohmann et al. [4]. A point distribution model introduced by Cootes et al. [5] computes the shape of sulcal basins across a training set. Any unlabelled sulcus can be expressed as a linear combination of the eigenvalues generated from the PCA of this shape covariance matrix; an optimization over the linear function gives the best label.
Spatial distribution models give spatial bounds but this is not adequate to discriminate between the sulci in a local region. They are usually combined with graphs which model connections between sulci thus giving local structural context. In the combined strategy, the spatial information is used to supply spatial priors [6], localization constraints or to narrow the search space in an optimization or graph matching process [7].
I will write about the use of graph models in my next post.
References
1) Georges Le Goualher, D. Louis Collins and Christian Barillot, Alan C. Evans, "Automatic Identification of Cortical Sulci Using a 3D Probabilistic Atlas," In MICCAI, 1998, pp. 509-518.
2) Georges Le Goualher, E. Procyk, D.L. Collins, R. Venugopal, Christian Barillot, "Automated Extraction and Variability Analysis of Sulcal Neuroanatomy," IEEE Trans. Med. Imag., 18(3), 1999, pp. 206-217.
3) A. C. Evans, D. L. Collins, P. Neelin, M. Kamber, S. Marrett, "Three-dimensional correlative imaging: Applications in human brain mapping," Advances in Functional NeuroImaging: Technical Foundations,(ed. R. Thatcher and M. Hallett and T. Zeffiro and E. John and M. Huerta) Academic Press, 1994, pp. 145-162.
4) Gabrielle Lohmann and Y. von Cramon, "Automatic labeling of the human cortical surface using sulcal basins," IEEE Trans. Med. Imag., 4, 2000, pp. 179-188.
5) Timothy F. Cootes, Christopher J. Taylor, David H. Cooper, Jim Graham, "Active Shape Models-Their Training and Application," Computer Vision and Image Understanding, 61(1), 1995, pp. 38-59.These two ideas, the use of the probabilistic atlas and the graph, have been incorporated into automated and semi-automated labeling methods in various ways. In this post I will present the basic idea behind the use of the spatial distribution model.
The use of a probabilistic atlas
Probabilistic maps compute the probability for each tissue class at every voxel location using a large database of segmented and labeled anatomical structures. Evans et al. [3] coined the name Statistical Probabilistic Anatomical Maps or SPAM for these models. Paul Thompson has a nice description of these SPAM models and the Brainvisa website has a nice visualization of a sulcal atlas which is reproduced below:
A straightforward implementation of the probabilistic atlas paradigm can be seen in Le Goualher et al. [1] [2]. SPAM models give the probability for each sulcal class so that at any given location, unlabeled sulci are assigned the most probable label for that location. In other words, to label a new sulcus
Let: 
be a sulcal label
Compute:
Assign:
The use of a point distribution model
A different spatial distribution model is used by Lohmann et al. [4]. A point distribution model introduced by Cootes et al. [5] computes the shape of sulcal basins across a training set. Any unlabelled sulcus can be expressed as a linear combination of the eigenvalues generated from the PCA of this shape covariance matrix; an optimization over the linear function gives the best label.
Spatial distribution models give spatial bounds but this is not adequate to discriminate between the sulci in a local region. They are usually combined with graphs which model connections between sulci thus giving local structural context. In the combined strategy, the spatial information is used to supply spatial priors [6], localization constraints or to narrow the search space in an optimization or graph matching process [7].
I will write about the use of graph models in my next post.
References
1) Georges Le Goualher, D. Louis Collins and Christian Barillot, Alan C. Evans, "Automatic Identification of Cortical Sulci Using a 3D Probabilistic Atlas," In MICCAI, 1998, pp. 509-518.
2) Georges Le Goualher, E. Procyk, D.L. Collins, R. Venugopal, Christian Barillot, "Automated Extraction and Variability Analysis of Sulcal Neuroanatomy," IEEE Trans. Med. Imag., 18(3), 1999, pp. 206-217.
3) A. C. Evans, D. L. Collins, P. Neelin, M. Kamber, S. Marrett, "Three-dimensional correlative imaging: Applications in human brain mapping," Advances in Functional NeuroImaging: Technical Foundations,(ed. R. Thatcher and M. Hallett and T. Zeffiro and E. John and M. Huerta) Academic Press, 1994, pp. 145-162.
4) Gabrielle Lohmann and Y. von Cramon, "Automatic labeling of the human cortical surface using sulcal basins," IEEE Trans. Med. Imag., 4, 2000, pp. 179-188.
6) M. Perrot, D. Rivière, J.-F. Mangin, "Identifying cortical sulci from localizations, shape and local organization," ISBI, 2008, pp. 420-423.
7) Yang, F & Kruggel, F., "A graph matching approach for labeling brain sulci using location, orientation, and shape," Neurocomputing, 2009, pp. 179-190.
Posts on Sulcal Labeling
1) Why we label sulci
2) Why is sulcal labeling difficult ?
3) The use of a spatial distribution model in labeling sulci
Wednesday, February 8, 2012
Why is sulcal labeling difficult?
This is a follow-up to an earlier post Why we label sulci. There will be two or three more posts; taken altogether, they will describe the sulcal labeling problem.
The labeling of sulci is a challenging problem. This is because, cortical sulci are highly variable. Sulci vary not just across individuals but even between the hemispheres of a single brain [1]. It might be useful when looking for ways to address this variability to classify this variation as follows:
Variation in physical features
Sulci vary in shape, in scale and in their placement (i.e. position and orientation) The figure below illustrates how the variability can make feature selection difficult.
The boxplot shows the length distribution for 18 subjects. The 10 types or classes of sulci shown cannot be identified solely on a length measurement. This poses a problem for feature selection and classification.
Figure credit: Meena Mani
Variation in branching
19th century illustrations such as those from Horsley [2], trace the wide variations along a sulcal fold. A whole nomenclature has developed since then to account for the branch variations possible along a single sulcus. (An example from the Ono atlas is illustrative--see figure below). For this reason, there is no gold standard in sulcal labeling; one neuroanatomist may disagree with another.
The figure to the left shows the pattern variations for a single sulcus (the posterior end of the superior frontal sulcus). Types A, B, C, D, are possible variations for this sulcus (for the 25 postmortem brains examined, 4 variations were found). The pattern in the two hemispheres of a single subject may differ; the left may be Type B and the right may be Type C. The lengths of the small segments and the connections they make to other sulci may also vary. Reproduced from Ono et al. [1].
Variation in number
Sulci may be continuous (present as one uninterrupted segment) in some individuals, fragmented (exist as multiple segments) in others and altogether absent in yet others. The larger primary sulci which start forming early in fetal development are the most consistent; the secondary and tertiary sulci are not always expressed.
References
1) Ono, M., Kubic, S. & Abernathy, C. (1892) "Atlas of the Cerebral Sulci", (Thieme, New York).
2) Horsley, V. (1892) "On the topographical relations of the cranium and surface of the cerebrum", In "Contribution to the surface anatomy of the cerebral hemispheres", pp.306-355, (Royal Irish Academy).
Posts on Sulcal Labeling
1) Why we label sulci
2) Why is sulcal labeling difficult ?
3) The use of a spatial distribution model in labeling sulci
The labeling of sulci is a challenging problem. This is because, cortical sulci are highly variable. Sulci vary not just across individuals but even between the hemispheres of a single brain [1]. It might be useful when looking for ways to address this variability to classify this variation as follows:
Variation in physical features
Sulci vary in shape, in scale and in their placement (i.e. position and orientation) The figure below illustrates how the variability can make feature selection difficult.
The boxplot shows the length distribution for 18 subjects. The 10 types or classes of sulci shown cannot be identified solely on a length measurement. This poses a problem for feature selection and classification.
Figure credit: Meena Mani
Variation in branching
19th century illustrations such as those from Horsley [2], trace the wide variations along a sulcal fold. A whole nomenclature has developed since then to account for the branch variations possible along a single sulcus. (An example from the Ono atlas is illustrative--see figure below). For this reason, there is no gold standard in sulcal labeling; one neuroanatomist may disagree with another.
The figure to the left shows the pattern variations for a single sulcus (the posterior end of the superior frontal sulcus). Types A, B, C, D, are possible variations for this sulcus (for the 25 postmortem brains examined, 4 variations were found). The pattern in the two hemispheres of a single subject may differ; the left may be Type B and the right may be Type C. The lengths of the small segments and the connections they make to other sulci may also vary. Reproduced from Ono et al. [1].
Variation in number
Sulci may be continuous (present as one uninterrupted segment) in some individuals, fragmented (exist as multiple segments) in others and altogether absent in yet others. The larger primary sulci which start forming early in fetal development are the most consistent; the secondary and tertiary sulci are not always expressed.
References
1) Ono, M., Kubic, S. & Abernathy, C. (1892) "Atlas of the Cerebral Sulci", (Thieme, New York).
2) Horsley, V. (1892) "On the topographical relations of the cranium and surface of the cerebrum", In "Contribution to the surface anatomy of the cerebral hemispheres", pp.306-355, (Royal Irish Academy).
Posts on Sulcal Labeling
1) Why we label sulci
2) Why is sulcal labeling difficult ?
3) The use of a spatial distribution model in labeling sulci
Wednesday, January 25, 2012
Medical visualization frontier application
The goal of medical imaging is to present the data in a useful format and in a very interesting TEDx talk Anders Ynnerman demonstrates cool new applications in medical visualization that will be possible in the near future.
Graphics processors have become substantially faster in the last ten years. We can now put together** the gigabytes (terabytes if extended to the time domain) of MRI and CT data generated when scanning a single subject and create 3D (or 4D) images from which relevant information can be selectively extracted. This opens up new and very interesting possibilities. One such application is the virtual autopsy where with ipad-style interactions one can look at cadavers in hard-to-maneuver angles or selectively view metal to, for instance, identify the extent of knife stab injuries or locate bullet shards. Ynnerman also suggests touch-sensitive haptic applications: a surgeon can literally touch the data--a beating heart for example--pre-surgery.
It's really a great 17 minute talk--here's the link.
**(Aside from fast GPUs, there are other ways in which people are hoping to handle the explosion of data from these medical scans. The use of oompressive sensing algorithms is one but the more general idea is to reduce the data before, during or after the scan.)
Graphics processors have become substantially faster in the last ten years. We can now put together** the gigabytes (terabytes if extended to the time domain) of MRI and CT data generated when scanning a single subject and create 3D (or 4D) images from which relevant information can be selectively extracted. This opens up new and very interesting possibilities. One such application is the virtual autopsy where with ipad-style interactions one can look at cadavers in hard-to-maneuver angles or selectively view metal to, for instance, identify the extent of knife stab injuries or locate bullet shards. Ynnerman also suggests touch-sensitive haptic applications: a surgeon can literally touch the data--a beating heart for example--pre-surgery.
It's really a great 17 minute talk--here's the link.
**(Aside from fast GPUs, there are other ways in which people are hoping to handle the explosion of data from these medical scans. The use of oompressive sensing algorithms is one but the more general idea is to reduce the data before, during or after the scan.)
Saturday, June 4, 2011
Shape analysis: on selecting a set of points or landmarks
One approach to shape analysis (after Kendall) uses a fixed number of points to define a shape. The points may describe an object boundary or an interior morphology such as the veins on a leaf. They may be selected randomly; alternatively they may be landmarks--i.e. points of significance. A set of points, so selected, constitutes a shape summary and the original shape data, that had been extracted from the raw image, is discarded.
One problem with representing a shape in this way is that it introduces a source of variability. Different shapes can be reconstructed with the same set of points. This is especially true if the number of points selected for that particular shape are few.
There are, of course, other approaches to shape analysis that do not involve selecting points (at least not at this stage of shape representation). Deformable templates is one such methodology and its use is quite common in medical image analysis. The shape could also be represented by a continuous function (see for example Younes et al. [1]). But these methods are also computationally expensive.
Reference
1) Younes, Laurent; Michor, Peter W.; Shah, Jayant; Mumford, David. A metric on shape space with explicit geodesics. Atti Accad. Naz. Lincei Cl. Sci. Fis. Mat. Natur. Rend. Lincei (9) Mat. Appl. 19 (2008), no. 1, 25--57.
One problem with representing a shape in this way is that it introduces a source of variability. Different shapes can be reconstructed with the same set of points. This is especially true if the number of points selected for that particular shape are few.
There are, of course, other approaches to shape analysis that do not involve selecting points (at least not at this stage of shape representation). Deformable templates is one such methodology and its use is quite common in medical image analysis. The shape could also be represented by a continuous function (see for example Younes et al. [1]). But these methods are also computationally expensive.
Reference
1) Younes, Laurent; Michor, Peter W.; Shah, Jayant; Mumford, David. A metric on shape space with explicit geodesics. Atti Accad. Naz. Lincei Cl. Sci. Fis. Mat. Natur. Rend. Lincei (9) Mat. Appl. 19 (2008), no. 1, 25--57.
Thursday, April 7, 2011
Anatomy of a good talk
I listened to a very interesting talk recently (via GoogleTech Talks--if the link is broken, search for Breakthroughs in Imaging Neurovascular diseases such as Multiple Sclerosis ...). Mark Haacke, the speaker, had some years ago developed Susceptibility Weighted Imaging (SWI)--an MR modality that uses the phase information in a signal to image iron deposits in the brain. His talk is interesting because there is a story, a narrative, built around this endeavour and his efforts to find important clinical applications. Indeed, he has linked his research to Paolo Zamboni's work with multiple sclerosis (MS). (There is a Facebook group advocating a Nobel prize for Zamboni so a significant impact is projected.)
For those of us in research it is always rewarding to witness--even from afar--the full lifecycle of an idea or concept. Haacke shows how he develops the method (it is really just another case of using parts of the signal that were being filtered out as noise); verifies (with Xray fluorescence--XRF) that what they are looking at is iron; looks for and finds iron deposits in the veins and brain tissue of patients with MS and other neurological diseases (which suggests that excess iron is a biomarker for these kinds of conditions); makes a case for the quantitative analysis of the venous system (the other half of the better explored arterial system); identifies new imaging applications such as the previously unseen microbleeds in patients with traumatic brain injury (TBI); presents temporal data to show the build-up of iron and finally links this aggregation with Zamboni's hypothesis that the narrowing or stenosis of veins (such as the internal jugular vein--IJV) that drain out of the brain, creates a reflux which subsequently results in the accumulation of iron.
It remains to be seen if multiple sclerosis and other neurodegenerative diseases have a vascular origin but at the very least, the evidence--presented in various ways in this talk--shows a strong cause.
Haacke ends by listing specific ways in which "technical people" can get involved. He suggests ways to quantify blood flow, develop biomarkers, track patients, develop databases and develop new sponsership models to fund all this work. It's a very complete talk in this sense and the right way to invite people with different kinds of expertise in.
The talk is pitched at a general audience but there is enough detail so that someone like me, who works in medical imaging and with MS datasets, can also benefit. I'm a street kid; there've been no mentors. And talks like this give a perspective I haven't been able to get anywhere else. But more pampered academics can also benefit. I've been reading a paper where the author dabbles in a whole lot of esoteric math but is unable to construct a biomarker that appeals to common sense. Well, having perspective is one way to compensate for a lack of common sense.
For those of us in research it is always rewarding to witness--even from afar--the full lifecycle of an idea or concept. Haacke shows how he develops the method (it is really just another case of using parts of the signal that were being filtered out as noise); verifies (with Xray fluorescence--XRF) that what they are looking at is iron; looks for and finds iron deposits in the veins and brain tissue of patients with MS and other neurological diseases (which suggests that excess iron is a biomarker for these kinds of conditions); makes a case for the quantitative analysis of the venous system (the other half of the better explored arterial system); identifies new imaging applications such as the previously unseen microbleeds in patients with traumatic brain injury (TBI); presents temporal data to show the build-up of iron and finally links this aggregation with Zamboni's hypothesis that the narrowing or stenosis of veins (such as the internal jugular vein--IJV) that drain out of the brain, creates a reflux which subsequently results in the accumulation of iron.
It remains to be seen if multiple sclerosis and other neurodegenerative diseases have a vascular origin but at the very least, the evidence--presented in various ways in this talk--shows a strong cause.
Haacke ends by listing specific ways in which "technical people" can get involved. He suggests ways to quantify blood flow, develop biomarkers, track patients, develop databases and develop new sponsership models to fund all this work. It's a very complete talk in this sense and the right way to invite people with different kinds of expertise in.
The talk is pitched at a general audience but there is enough detail so that someone like me, who works in medical imaging and with MS datasets, can also benefit. I'm a street kid; there've been no mentors. And talks like this give a perspective I haven't been able to get anywhere else. But more pampered academics can also benefit. I've been reading a paper where the author dabbles in a whole lot of esoteric math but is unable to construct a biomarker that appeals to common sense. Well, having perspective is one way to compensate for a lack of common sense.
Friday, February 11, 2011
Why we label sulci
The cortical surface is characterized by alternating ridges and furrows. The sulci (singular sulcus), as the Latin suggests, are the fissures or grooves; they serve as counterpoint to
the raised gyri (see the figure; some of the primary sulci are highligted in color). The sulci, in a sense, exist because they do not exist. Their utility derives from this fact as demonstrated by the following:
1) In neurosurgery they function as channels which give a surgeon access to parts of the brain even deep within the subcortex. As M.G. Yaşargil, a noted neurosurgeon, writes in the foreword to the Ono atlas [1]: "any point within the cranium can be reached by following the corridors of the sulci." Tissue damage from incisions is thus minimized.
2) They also serve as orienting landmarks in neurosurgery. The major sulci, in addition, partition important functional areas of the brain. This information reinforces their usefulness as landmarks. The central sulcus, for instance, demarcates the sensory-motor cortex. The sylvian fissure, one of the most identifiable cortical features, is the locus of language cortex. Both these sulci are important reference points in a variety of contexts and applications.
3) The sulcal grooves are filled with cerebrospinal fluid (CSF) which make them easy to identify in T1-weighted images (where CSF is dark in contrast to the brighter gray/white matter.)
To be useful in the neurosurgical applications described, we first need to identify and label the sulci. There are other applications that would also benefit from a reliable labeling scheme. Internal changes in the brain, either due to aging or pathology, for instance, alter the cortical surface. Labeling is the first step in a systematic study that allows us to quantify these changes for the differential diagnosis of disease.
Reference
1) Ono, M., Kubic, S. & Abernathy, C. (1990) "Atlas of the Cerebral Sulci", (Thieme, New York).
Posts on Sulcal Labeling
1) Why we label sulci
2) Why is sulcal labeling difficult ?
3) The use of a spatial distribution model in labeling sulci
the raised gyri (see the figure; some of the primary sulci are highligted in color). The sulci, in a sense, exist because they do not exist. Their utility derives from this fact as demonstrated by the following:
1) In neurosurgery they function as channels which give a surgeon access to parts of the brain even deep within the subcortex. As M.G. Yaşargil, a noted neurosurgeon, writes in the foreword to the Ono atlas [1]: "any point within the cranium can be reached by following the corridors of the sulci." Tissue damage from incisions is thus minimized.
2) They also serve as orienting landmarks in neurosurgery. The major sulci, in addition, partition important functional areas of the brain. This information reinforces their usefulness as landmarks. The central sulcus, for instance, demarcates the sensory-motor cortex. The sylvian fissure, one of the most identifiable cortical features, is the locus of language cortex. Both these sulci are important reference points in a variety of contexts and applications.
3) The sulcal grooves are filled with cerebrospinal fluid (CSF) which make them easy to identify in T1-weighted images (where CSF is dark in contrast to the brighter gray/white matter.)
To be useful in the neurosurgical applications described, we first need to identify and label the sulci. There are other applications that would also benefit from a reliable labeling scheme. Internal changes in the brain, either due to aging or pathology, for instance, alter the cortical surface. Labeling is the first step in a systematic study that allows us to quantify these changes for the differential diagnosis of disease.
Reference
1) Ono, M., Kubic, S. & Abernathy, C. (1990) "Atlas of the Cerebral Sulci", (Thieme, New York).
Posts on Sulcal Labeling
1) Why we label sulci
2) Why is sulcal labeling difficult ?
3) The use of a spatial distribution model in labeling sulci
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