1. Atlas-based Auto-Segmentation in Head and Neck
Cancer Radiotherapy: Clinical Benefits and Challenges
PRESENTED BY
DAYAWOTI PEGU
RESIDENT MEDICAL PHYSICIST, HBCH&RC VIZAG
GUIDED BY
M ANIL KUMAR
MEDICAL PHYSICIST, HBCH&RC VIZAG
2. OUTLINES
Introduction
Atlas based auto segmentation
Importance of auto-segmentation
Aim of the project
Material and Method
Results and discussion
conclusion
3. Introduction
Radiotherapy treatment planning is a time consuming process.
The target volumes and organs-at-risks(OAR) are manually delineated for treatment plan
generation.
In head and neck cancers, accurate contour delineation is essential for a good treatment
outcome.
To improve contouring efficiency and reduce potential inter-observer variation, the atlas-
based auto-segmentation function was introduced.
4. ATLAS BASED AUTO SEGMENTATION
An atlas is a predefined volume containing a set of predefined structures. An atlas set contains several different atlases that all
have a particular area in common.
Atlas-based auto segmentation is used to automatically contour target tumors and normal tissues on the CT images of a new
patient using predefined atlases and a non rigid registration technique.
Atlas based auto contouring is two types
1. Single atlas set
2. Multi atlas
The single atlas based procedure has the disadvantage of selecting only one atlas that is similar to the patient. It is not
practically possible to register and choose an atlas that roughly matches the patient with respect to all regions. Because of
which the auto-contouring accuracy becomes low. In multi- atlas based procedure, the auto-contouring accuracy will be more,
because the most similar structure can be selected from multiple structures.
Velocity- single atlas based auto-segmentation
SmartAdapt- multi atlas based auto-segmentation.
5. Importance of auto segmentation
Interobserver variations.
Substantial time.
OAR’S may not be routinely contoured.
The development of computational tools to automatically generate OAR contours can reduce
the time and effort required for HNC contouring.
6. Literature review on inter-observer variations
OAR’S DSC MDA(mm) HD(mm)
Brainstem 0.88(0.61-0.92) 1.5(1.1-4) 4(2.3-15)
Spinal cord 0.78(0.56-0.90) 2.2(0.8-10.4) 12.1(1.7-72.1)
Mandible 0.90(0.79-0.94) 1.1(0.8-8.3) 3.4(1.5-38.7)
Oral cavity 0.77(0.45-0.91) 4.6(1.8-11.6) 14.5(4.3-30.1)
Parotid R 0.83(0.51-0.90) 2(1.4-4.9) 5.1(3.2-19.2)
Parotid L 0.82(0.62-0.88) 1.9(1.2-4.2) 4.9(3.1-16.5)
Optic nerve R 0.59(0.55-0.64) 1.1(0.7-1.3) 6.5(4.1-7.7)
Optic nerve L 0.59(0.52-0.63) 1.0(0.9-1.4) 6.9(5.1-8.2)
Ebbe Laugaard Lorenzen et all.
https://doi.org/10.1080/0284186X.2021.1975813
https://doi.org/10.1186/s13014-020-01677-2
7. Aim
The aim of the project is to compare the
accuracy of Atlas-based auto-
segmentation of two applications, i.e.,
SmartAdapt and Velocity, at the head
and neck sites.
To analyze dosimetric impact of auto-
segmented contours against manually
segmented clinical contours.
8. methodology
85 hypopharynx Patients (till Nov 2023). 50 patients selected for creating atlas and 12 patients kept for
validation.
Patient Selection criteria for creating an atlas
1. Age 30 to 70 years
2. Distance between chin to sternum notch is 7 to 10 cm
5. BMI from 15 to 25
In smartAdapt, selected top five patients with highest similarity for segmentation.
9.
10. In velocity provides tools to improve the atlas based contouring
1. Model based segmentation
Brainstem Refined
Spinal cord Refined
2. Local deformable registration
Brainstem Shaped
Spinal cord Shaped
On comparison, Procedure 2 was found to give better segmentation accuracy and hence was used
for comparison with smartAdapt®
The auto-segmented contours were taken for dosimetric evaluation.
Time taken for Manual and the two auto-contouring systems were compared.
12. OAR’S for Auto segmentation
hypopharynx
1. Brainstem
2. Spinal cord
3. Oral cavity
4. Eye R
5. Eye L
6. Parotid R
7. Parotid L
8. Mandible
9. Optic nerve R
10. Optic nerve L
11.Esophagus
13. PARAMETERS FOR EVALUATION
Dice similarity coefficient( DSC)
Hausdorff distance (HD)
Relative volume difference ( RVD)
Mean distance to agreement( MDA)
Acceptance criteria according to ‘AAPM TG-132 report’
Geometric parameters Tolerance
DSC ~0.80–0.90
HD ~2–3 mm
MDA ~2–3 mm
RVD ~0
15. thesimilarity ofXandYis determinedaccordingto thedistanceofthenearestmaximumdistance.
nearestmaximum distance.HDisthusdefinedas,
Hausdorffdistance(HD)=max(d(X,Y),d(X,Y)
Whered(X,Y)isthedirectedHDfromXtoYandisgiven by
d=max(min(||x-y||)
x X,andy Y
AstheHDapproacheszeros,thedifferencebetweenthemanualcontouringandauto contouring
contouringandauto contouringbecomessmaller.Bycontrast,ifthecoefficientisgreaterthanzero,
coefficientisgreaterthanzero,thesimilaritybetweenthetwo volumesdecreases.
Hausdorffdistance(HD):
16. Mean distance to agreement(MDA)
Mean distance to agreement is the mean voxel wise comparison of distance between two
associative points in the contour sets A and B, defined by
MDA(A,B) = mean a ЄA,bЄB {d(a,B)ud (b,A)}
and denotes a measure of average similarity between two contour sets. A higher MDA between
two sets A and B indicates the existence of regions of dissimilarity between the two sets, where a
MDA of zero indicates that the sets A and B are identical. Because MDA represents an average
across all points in the sets A and B, MDA is less sensitive than HD to small pockets of high
dissimilarity.
17.
18. Statistical analysis
Null hypothesis 1: Geometrically, there is no significant difference in two systems i.e smartAdapt and
velocity.
Null hypothesis 2 : Dosimetrically, there is no significant difference between manual contours and auto-
segmented contours.
The Wilcoxon signed-rank test was used to compare the DSC,HD,MDA and RVD values of smart adapt and
velocity.
The Wilcoxon signed-rank test was used to compare between dosimetric parameters of auto segmented
OAR’s with respect to the manual clinical contour.
A p-value of less than 0.05 was considered statistically significant.
23. HD difference for spinal cord in
SmartAdapt
Manual contoured
Auto segmented
contoured
Manual contoured
Auto segmented
contoured
HD difference for spinal cord in velocity
24. The dose volume histograms (DVHs), were calculated for manual contours and auto
contours.
Determined whether the differences between the manual and auto-segmented contours
were statistically significant.
Dosimetric analysis
25.
26. ▲ manual contours
■ auto-segmented contours
Spinal cord
mandible
Oral cavity
Parotid R
Parotid L
Brainstem
DVH of SmartAdapt
32. Time analysis
The time required to perform the auto contouring and manual contouring was measured.
SmartAdapt – ranges from 2 min 20 seconds to 4 minute 23 seconds
Velocity – ranges from 2 minutes 21 seconds to 2 min 50 seconds
Manual – ranges from 16 to 20 min
33. Discussions
Studies have reported that it is essential to have at least a similar head position between atlas and sample patients if not
a perfect chin match, for an acceptable mandible contour. By restricting chin to sternum notch distance to 7-10 cm in our
atlas sets we overcame this challenge.
Accuracy of oral cavity automatic contours were highly influenced by mandible or head position. Hence oral cavity is
giving good accuracy.
Atlas in both smartadapt and velocity were first created using stringent criteria of age (30-65 years), then created and
atlas set with sample size of 30 patients. No substantial difference found from 50 patient sample.
By increasing the number of atlas size to create atlas sets, there was no effect in geometric parameters.
The inconsistency in relatively small organs for HN subjects may be attributed to the specific atlas selection method
where global intensity similarity is used as the matching metric and consequently , the contributions of relatively small
local regions are discounted.
Example : optic nerves
34. Discussions
The geometric discrepancy between AS and MS contours may have been caused by the inconsistencies in MS
contours. For example large variations in the superior-inferior ranges of cord.
Cervical oesophagus is relatively low intensity structures in a low contrast region and also subject to movement.
Hence segmentation becomes difficult and thus had the least DSC score and high HD value among all structures.
The dosimetric differences of organs with low geometric accuracies such as optic nerves are relatively small
between AS and MS because they are located distant to the target and high dose region.
If an organ is located in a high dose region with low dose gradient, its dosimetric metrics may have high absolute
values, but minor variation related to geometric shape change.
A larger pool of patient samples in future studies would be beneficial to characterize the dosimetry performance of
each individual structure.
35. CONCLUSION
At least a similar setup is an essential pre-requisite to generate an acceptable set of automatic contours in
single atlas based systems.
The dosimetry performance not only depends on geometric accuracy, but is also heavily impacted by
spatial dose distribution and gradient.
The geometric measures alone were not sufficient to predict the dosimetric impact of segmentation
inaccuracies on RT treatment plans.
36. References
Cao M, Stiehl B, Yu VY, Sheng K, Kishan AU, Chin RK, Yang Y, Ruan D. Analysis of geometric performance and
dosimetric impact of using automatic contour segmentation for radiotherapy planning. Frontiers in
oncology. 2020 Sep 23;10:1762.
Hu Y, Byrne M, Archibald‐Heeren B, Thompson K, Fong A, Knesl M, Teh A, Tiong E, Foster R, Melnyk P, Burr
M. Implementing user‐defined atlas‐based auto‐segmentation for a large multi‐centre organisation: the
Australian Experience. Journal of Medical Radiation Sciences. 2019 Dec;66(4):238-49.
van der Veen J, Gulyban A, Willems S, Maes F, Nuyts S. Interobserver variability in organ at risk delineation in
head and neck cancer. Radiation Oncology. 2021 Dec;16:1-1.
Lorenzen EL, Kallehauge JF, Byskov CS, Dahlrot RH, Haslund CA, Guldberg TL, Lassen-Ramshad Y, Lukacova
S, Muhic A, Witt Nyström P, Haldbo-Classen L. A national study on the inter-observer variability in the
delineation of organs at risk in the brain. Acta Oncologica. 2021 Nov 2;60(11):1548-54.
37. ACKNOWLEDGEMENTS:
Mr. M Anil Kumar
Mr. Raghavendra Hajare
Ms. KK Sreelakshmi
Dr. Rohit Vadgoankar
Dr. Kiriti Chiriki
Dr. Chandrasekhar pusarla
