Comparison and evaluation of methods for liver segmentation from CT datasets.
This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques.
Heimann
Tobias
T
Division of Medical and Biological Informatics, German Cancer Research Center, 69121 Heidelberg, Germany. t.heimann@dkfz.de
van Ginneken
Bram
B
Styner
Martin A
MA
Arzhaeva
Yulia
Y
Aurich
Volker
V
Bauer
Christian
C
Beck
Andreas
A
Becker
Christoph
C
Beichel
Reinhard
R
Bekes
György
G
Bello
Fernando
F
Binnig
Gerd
G
Bischof
Horst
H
Bornik
Alexander
A
Cashman
Peter M M
PM
Chi
Ying
Y
Cordova
Andrés
A
Dawant
Benoit M
BM
Fidrich
Márta
M
Furst
Jacob D
JD
Furukawa
Daisuke
D
Grenacher
Lars
L
Hornegger
Joachim
J
Kainmüller
Dagmar
D
Kitney
Richard I
RI
Kobatake
Hidefumi
H
Lamecker
Hans
H
Lange
Thomas
T
Lee
Jeongjin
J
Lennon
Brian
B
Li
Rui
R
Li
Senhu
S
Meinzer
Hans-Peter
HP
Nemeth
Gábor
G
Raicu
Daniela S
DS
Rau
Anne-Mareike
AM
van Rikxoort
Eva M
EM
Rousson
Mikaël
M
Rusko
László
L
Saddi
Kinda A
KA
Schmidt
Günter
G
Seghers
Dieter
D
Shimizu
Akinobu
A
Slagmolen
Pieter
P
Sorantin
Erich
E
Soza
Grzegorz
G
Susomboon
Ruchaneewan
R
Waite
Jonathan M
JM
Wimmer
Andreas
A
Wolf
Ivo
I
D000465:Algorithms; D001499:Bayes Theorem; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008099:Liver / Q000033:anatomy & histology*; D014057:Tomography, X-Ray Computed / Q000379:methods*