Master Research Presentation

Automated Measurement of Brain Volume in Patients after aneurysmal

Subarachnoid HemorrhageAnne Kaspers

source: socialmediaseo.net

Contents

• Introduction• Methods

• Data

• Routine

• Evaluation

• Results• Discussion

• Classification issues

• Strength and limitations

• Conclusion• Questions

Introduction

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

• What is aSAH?

• After aSAH: brain damage

source: thestrokefoundation.comsource: socialmediaseo.net

Introduction

• Annual incidence: 6 - 16 cases per 100,000

• Fatality rate: 50 percent

• 50 percent of the survivors suffer from neurological or

cognitive deficits after a year

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Introduction

• Purpose: mapping brain volume

• A new routine is needed

• for accurate brain volume measurement for 3 T MR

images

• for cerebral abnormalities

• The routine is based on kNN using manually segmented

MR image training data

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Data

• Axial T1-weighted and T2-weighted images from a aSAH

study 1.

• 10 training and 12 validation scans of patients after aSAH

and control participants

• Exclusion of patients with claustrophobia, neurosurgical

clips, pacemaker, younger than 18 years

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

1 Schaafsma JD et al. (2010) Intracranial aneurysms treated with coil placement: test characteristics of follow-up MR angiography--multicenter study. Radiology 1:209-218

Methods - Routine

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

T1 and T2 weighted image

Input images

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

Registered T1 weighted imageand T2 weighted image

Registration

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

Create mask to- include brain tissue- exclude skull and fatty tissue

Mask Creation

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

Image of 10 clusters

Perform k-means

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

Selection of clusters and mask

Create Mask

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

Mask of the cerebrum on the T2 weigthed image

Remove Cerebellum

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

Extracted Brain in the T1 and T2 weighted image

Extract Brain Images

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

T2 weighted image with and without shading

Shading Correction

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

kNN Classification

Methods - Routine

• Training data

• 10 full segmentations

• Subcortical structures, cortical grey matter,

peripheral CSF and lateral ventricles

• Only voxels without partial volume effect

No partial volume effect

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• A sample consist of a location, intensities and a label• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

T1 T2

y

xz

x

y

zx

Methods - Routine

• What is feature space?

Inte

nsity

Location

Sample of Structure 1 Sample of Structure 2

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• How does k-Nearest Neigbor (kNN) work?

Inte

nsity

Location

Sample of Structure 1 Sample of Structure 2 New Sample

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• How does k-Nearest Neigbor (kNN) work?

Inte

nsity

Location

Sample of Structure 1 Sample of Structure 2 New Sample

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• How does k-Nearest Neigbor (kNN) work?

Inte

nsity

Location

Sample of Structure 1 Sample of Structure 2 New Sample

k = 1

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• How does k-Nearest Neigbor (kNN) work?

Inte

nsity

Location

Sample of Structure 1 Sample of Structure 2 New Sample

k = 1

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• How does k-Nearest Neigbor (kNN) work?

Inte

nsity

Location

Sample of Structure 1 Sample of Structure 2 New Sample

k = 3

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• How does k-Nearest Neigbor (kNN) work?

Inte

nsity

Location

Sample of Structure 1 Sample of Structure 2 New Sample

k = 3

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Methods - Routine

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Remove Edge for subcortical structures and cortical grey matter

Methods - Routine

Move back CSF from lateral ventricles

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Lateral ventricles before and after transfer CSF

Methods - Routine

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Remove Infarcts

Methods - Routine

Final result

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Method - Evaluation

• Validation by 2 observers

• Half slices selected throughout the brain

T2 weighted image, Subcortical structures, Cortical grey matter, Peripheral CSF and Lateral ventricles

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Method - Evaluation

• Manual fraction combines information of multiple

observers and multiple structures

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

T2 weighted image, Subcortical structures, Cortical grey matter, Peripheral CSF and Lateral ventricles

Method - Evaluation

• Inter-observer agreement

Observer segmentations

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Method - Evaluation

• Routine validation

Observer segmentations

Routine segmentations

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Method - Evaluation

• Measuring agreement using:

• Fractional Similarity Index (fSI)

• Sensitivity and Specificity

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Results

• Inter-observer agreement• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Results

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

• Inter-observer agreement good for most structures

(fSI > 0.80)

Results

• Routine validation results• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Results

• Routine agreement good for subcortical structures, lateral

ventricles, total brain and intracranial volume

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Results

• Routine agreement good for subcortical structures, lateral

ventricles, total brain and intracranial volume

• Cortical grey matter, peripheral and total CSF fSI scores

lower

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Discussion – Classification issues

• Low scores of cortical grey matter because of :

• Slice thickness larger than structure thickness

• Unclear border

• Perivascular spaces

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Discussion – Classification issues

• Low scores of peripheral CSF because of

• Slice thickness larger than structure thickness

• Under-segmentation in training data

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Discussion – Strength and limitations

• Strength:

• fSI could better deal with probabilities

• Limitation:

• fractional observer values limited

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Conclusion

• Automated routine is accurate for lateral ventricles,

total brain and intracranial volume

• It could be used for volume measurements in patients

after aSAH

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Acknowledgments

ISI

Nelly AnbeekJeroen de BresserHugo KuijfMax ViergeverKoen Vincken

Neurology

Geert Jan BiesselsGabriël RinkelJoanna Schaafsma

Others

Marja van AkenEkke KaspersBart Waalewijn

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions

Questions

• Introduction

• Methods

• Data

• Routine

• Evaluation

• Results

• Discussion

• Classification

• Strength and limitations

• Conclusion

• Questions