Application
Results
Automatic segmentation of liver tumors from
MRI images.
Rune Petter Sørlie
Department of Informatics
University of Oslo
IVS student presentations, spring 2005
Summary
Application
Results
Outline
1
Application
Treating liver tumors
Guidance of probes
Monitoring the process
2
Results
Segmentation Results
Evaluation of segmentation
Summary
Application
Results
Methods to destroy a tumor.
We will show two probe based methods which allow minimum
invasive treatment.
Cryoablation
RF ablation
Pros and cons
Less risk of infections.
Patient quicker back in business.
Not suitable for tumors >3cm.
High rate of metastases reoccurances (Cryo).
Fails to damage cells near major vessels.
Summary
Application
Results
Cryoablation
Cryoablation uses liquid nitrogen to freeze the tisse
surrounding the probe. At about -40◦ C the cell membrane
cracks and the cells are damaged.
Summary
Application
Results
Cryoablation
Typical tumor is 2-3cm and a rim of 1cm around the tumor is
frozen.
Summary
Application
Results
Summary
RF ablation
Makes use of Radio Frequency electro magnetic waves to heat
up tissue. Cures tumors <3cm.
Application
Results
Summary
Guidance and probes
Both methods make use of probes. The surgeon want guidance
when inserting the probe into the tumor. Different modalities
are used
A combination of CT and ultrasound
Fluoroscopy(X-ray)
IVS use the Open MR scanner.
Application
Principal of RF ablation
Results
Summary
Application
Diagnostic image/pre surgery
Results
Summary
Application
Guidance of the RF-probe
Results
Summary
Application
24 hours after treatment
Results
Summary
Application
Results
9 months later, complete treatment
Summary
Application
Watershed segmentation
Results
Summary
Application
Level set segmentation
Results
Summary
Application
Results
Summary
Improved preprocessing
Conventional preprocessing by smoothing with a 3x3 mean
filter.
Important edges are perpendicular to the radius. By
filtering the radial line in polarcoordinates we may focus on
the edge of interest.
Application
Results
Summary
...cont. Improved preprocessing
Z
~
F (x) =
f (~k ) e−j2πk ·~x d 3~k , −∞ ≤ k ≤ ∞
(1)
kφ T
~k = kθ kr (2)
R3
φ ~x = θ r Z
F (x) =
F (x) =
f (kr ) e−j2πkr ·r dkr , −∞ ≤ k ≤ ∞
X
i
f (kr ) e−j2πkr ·ri dkr , 0 ≤ k ≤ π
(3)
(4)
Application
Results
Summary
The segmentation evaluation method.
For evaluaton of the segmentation I used Dice Similarity
Coefficient (DSC). The method finds relative overlap.
The improved preprocessing provided no more than similar
results as traditional methods.
Application
Results
Summary
Summary
Most cases possible to segment automatic.
Quality of segmentation close to manual segmentation.
Outlook
Make adaptive parametersettings.
Apply morphological dilation to improve segmentation.
Evaluate segmentation with a probe inserted into the tumor
(requires preprocessing).
Application
Results
Questions?
Summary