1

Interactive Medical Image Segmentation using Deep
Learning with Image-specific Fine-tuning

arXiv:1710.04043v1 [] 11 Oct 2017

Guotai Wang, Wenqi Li, Maria A. Zuluaga, Rosalind Pratt, Premal A. Patel, Michael Aertsen, Tom Doel, Anna L.
David, Jan Deprest, Sébastien Ourselin, and Tom Vercauteren

Abstract—Convolutional neural networks (CNNs) have
achieved state-of-the-art performance for automatic medical
image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition,
they are limited by the lack of image-specific adaptation and
the lack of generalizability to previously unseen object classes.
To address these problems, we propose a novel deep learningbased framework for interactive segmentation by incorporating
CNNs into a bounding box and scribble-based segmentation
pipeline. We propose image-specific fine-tuning to make a CNN
model adaptive to a specific test image, which can be either
unsupervised (without additional user interactions) or supervised
(with additional scribbles). We also propose a weighted loss
function considering network and interaction-based uncertainty
for the fine-tuning. We applied this framework to two applications: 2D segmentation of multiple organs from fetal MR
slices, where only two types of these organs were annotated for
training; and 3D segmentation of brain tumor core (excluding
edema) and whole brain tumor (including edema) from different
MR sequences, where only tumor cores in one MR sequence
were annotated for training. Experimental results show that
1) our model is more robust to segment previously unseen
objects than state-of-the-art CNNs; 2) image-specific fine-tuning
with the proposed weighted loss function significantly improves
segmentation accuracy; and 3) our method leads to accurate
results with fewer user interactions and less user time than
traditional interactive segmentation methods.
Index Terms—Interactive image segmentation, convolutional
neural network, fine-tuning, fetal MRI, brain tumor

I. I NTRODUCTION

D

EEP learning with convolutional neural networks
(CNNs) has achieved state-of-the-art performance for automated medical image segmentation [1]. However, automatic
segmentation methods have not demonstrated sufficiently accurate and robust results for clinical use due to the inherent
challenges of medical images, such as poor image quality,
different imaging and segmentation protocols, and variations
among patients [2]. Alternatively, interactive segmentation
methods are widely adopted, as integrating the user’s knowledge can take into account the application requirements and
G. Wang, W. Li, M A. Zuluaga, R. Pratt, P. Patel, T. Doel, S. Ourselin and
T. Vercauteren are with the Department of Medical Physics and Biomedical
Engineering, and Wellcome EPSRC Centre for Interventional and Surgical
Sciences (WEISS), University College London, London, WC1E 6BT, UK. T.
Vercauteren is also with KU Leuven. e-mail: guotai.wang.14@ucl.ac.uk.
A L. David is with WEISS and Institute for Women’s Health, University
College London. M. Aertsen and J. Deprest are with University Hospitals KU
Leuven. J. Deprest is also with WEISS.
This work has been submitted to the IEEE for possible publication.
Copyright may be transferred without notice, after which this version may
no longer be accessible.

make it easier to distinguish different tissues [2]–[4]. As such,
interactive segmentation remains the state of the art for existing commercial surgical planning and navigation products.
Though leveraging user interactions often leads to more robust
segmentations, a good interactive method should require as
little user time as possible to reduce the burden on users.
Motivated by these observations, we investigate combining
CNNs with user interactions for medical image segmentation to achieve higher segmentation accuracy and robustness
with fewer user interactions and less user time. However,
there are very few studies on using CNNs for interactive
segmentation [5]–[7]. This is mainly due to the requirement
of large amounts of annotated images for training, the lack of
image-specific adaptation and the demanding balance of model
complexity, time and memory space efficiency.
The first challenge of using CNNs for interactive segmentation is that current CNNs do not generalize well to previously
unseen object classes, as they require labeled instances of each
object class to be present in the training set. For medical
images, annotations are often expensive to acquire as both
expertise and time are needed to produce accurate annotations.
This limits the performance of CNNs to segment objects for
which annotations are not available in the training stage.
Second, interactive segmentation often requires imagespecific learning to deal with large context variations among
different images, but current CNNs are not adaptive to different test images, as parameters of the model are learned
from training images and then fixed during the testing, without
image-specific adaptation. It has been shown that imagespecific adaptation of a pre-trained Gaussian Mixture Model
(GMM) helps to improve segmentation accuracy [8]. However,
transitioning from simple GMMs to powerful but complex
CNNs in this context has not yet been demonstrated.
Third, fast inference and memory efficiency are demanded
for interactive methods. These can be relatively easily achieved
for 2D segmentations, but become much more problematic
for 3D volumes. For example, DeepMedic [9] works on local
patches to reduce memory requirements but results in a slow
inference. HighRes3DNet [10] works on an entire volume with
relatively fast inference but needs a large amount of GPU
memory, leading to high hardware requirements. To make a
CNN-based interactive segmentation method efficient to use,
enabling CNNs to respond quickly to user interactions and
to work on a machine with limited GPU resources (e.g, a
standard desktop PC or a laptop) is desirable. DeepIGeoS [7]
combines CNNs with user interactions and has demonstrated
good interactivity. However, it has a lack of adaptability to

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unseen image contexts.

A. Contributions
The contributions of this work are four-fold. First, we propose a novel deep learning-based framework for interactive 2D
and 3D medical image segmentation by incorporating CNNs
into a bounding box and scribble-based binary segmentation
pipeline. Second, we propose to use image-specific fine-tuning
to adapt a CNN model to each test image independently. The
fine-tuning can be either unsupervised (without additional user
interactions) or supervised where user-provided scribbles will
guide the learning process. Third, we propose a weighted
loss function considering network and interaction-based uncertainty during image-specific fine-tuning. Fourth, we present the
first attempt to employ CNNs to segment previously unseen
objects. The proposed framework does not require annotations
of all the organs for training. Thus, it can be applied to new
organs or new segmentation protocols directly.

B. Related Works
1) CNNs for Image Segmentation: For natural image segmentation, FCN [11] and DeepLab [12] are among the
state-of-the-art performing methods. For 2D biomedical image segmentation, efficient networks such as U-Net [13],
DCAN [14] and Nabla-net [15] have been proposed. For 3D
volumes, patch-based CNNs were proposed for segmentation
of the brain tumor [9] and pancreas [16], and more powerful
end-to-end 3D CNNs were proposed by V-Net [17], HighRes3DNet [10], and 3D deeply supervised network [18].
2) Interactive Segmentation Methods: An extensive range
of interactive segmentation methods have been proposed [2].
Representative methods include Graph Cuts [19], Random
Walks [3] and GeoS [4]. Machine learning methods have
been widely used to achieve high accuracy and interaction
efficiency. For example, GMMs are used by GrabCut [20]
to segment color images. Online random forests (ORFs) are
employed by SlicSeg [21] for segmentation of fetal MRI volumes. In [22], active learning is used to segment 3D Computed
Tomography (CT) images. They have achieved more accurate
segmentations with fewer user interactions compared with
traditional interactive segmentation methods.
To combine user interactions with CNNs, DeepCut [5] and
ScribbleSup [23] propose to leverage user-provided bounding
boxes or scribbles, but they employ user interactions as sparse
annotations for the training set rather than as guidance for
dealing with a single test image. 3D U-Net [24] learns from
annotations of some slices in a volume and produces a dense
3D segmentation, but takes a long time for training and cannot
be made responsive to user interactions. In [6], an FCN is
combined with user interactions for 2D RGB image segmentation, without adaptation for medical images. DeepIGeoS [7]
uses geodesic distance transforms of scribbles as additional
channels of CNNs for interactive medical image segmentation,
but cannot deal with previously unseen object classes.

3) Model Adaptation: Previous learning-based interactive
segmentation methods often employ an image-specific model.
For example, GrabCut [20] and SlicSeg [21] learn from the
target image with GMMs and ORFs, respectively, so that they
can be well adapted to the specific target image. Learning
a model from a training set with image-specific adaptation
during the testing has also been used to improve the segmentation performance. For example, an adaptive GMM has been
used to address the distribution mismatch between a test image
and the training set [8]. For CNNs, fine-tuning [25] is used
for domain-wise model adaptation to address the distribution
mismatch between different training sets. However, to the best
of our knowledge, this paper is the first work to propose imagespecific model adaptation for CNNs.
II. M ETHOD
The proposed interactive segmentation framework is depicted in Fig. 1. We refer to it as BIFSeg. To deal with different
(including previously unseen) objects in a unified framework,
we propose to use a CNN that takes as input the content of a
bounding box of one instance and gives a binary segmentation.
During the test stage, the bounding box is provided by the user,
and the segmentation and the CNN are alternatively refined
through unsupervised (without additional user interactions) or
supervised (with user-provided scribbles) image-specific finetuning. Our framework is general, flexible and can handle both
2D and 3D segmentations with few assumptions of network
structures. In this paper, we choose to use the state-of-the-art
network structures proposed in [7]. The contribution of BIFSeg
is nonetheless largely different from [7] as BIFSeg focuses
on segmentation of previously unseen object classes and finetunes the CNN model on the fly for image-wise adaptation
that can be guided by user interactions.
A. CNN Models
For 2D images, we adopt the P-Net [7] for bounding
box-based binary segmentation. The network is resolutionpreserving using dilated convolution [12] to avoid potential
loss of details. As shown in Fig. 2(a), it consists of six blocks
with a receptive field of 181×181. The first five blocks have
dilation parameters of 1, 2, 4, 8 and 16, respectively, so they
capture features at different scales. Features from these five
blocks are concatenated and fed into block6 that serves as a
classifier. A softmax layer is used to obtain probability-like
outputs. In the testing stage, we update the model based on
image-specific fine-tuning. To ensure efficient fine-tuning and
fast response to user interactions, we only fine-tune parameters
of the classifier (block6). Thus, features in the concatenation
layer for the test image can be stored before the fine-tuning.
For 3D images, we consider a trade-off between receptive
field, inference time and memory efficiency. As shown in
Fig. 2(b), the network is similar to P-Net. It has an anisotropic
receptive field 85×85×9. Compared with slice-based networks, it employs 3D context. Compared with large isotropic
3D receptive fields [10], it has less memory consumption
during inference [26]. Besides, anisotropic acquisition is often
used in MR images. We use 3×3×3 kernels in the first two

3

…

…

Training
stage

Trained CNN
model

θ0
Cropped training images

Training images

Pre-trained
CNN model

Testing
stage

Image-specific finetuning with weighted
loss function

Updated CNN
model

Weight map

Refined result

θ1

θ0
Image with user-provided
bounding box
Initial result

Scribbles

3

3

3

4

4

4

5

5

5

3x3x3, 64

3x3x3, 64

3x3x3, 64

3x3x1, 64

3x3x1, 64

3x3x1, 64

3x3x1, 64

3x3x1, 64

3x3x1, 64

3x3x1, 64

3x3x1, 64

3x3x1, 64

Input Image

2

Block 3

Block 4

Block 5

1

(a) P-Net for segmentation of a 2D slice

1

1

1

1

1

1x1x1, 2

1

1x1x1, 16

1

Output

Block 6

Block 6

1x1x1, 16

3x3, 64

2

1x1x1, 16

3x3, 64

1

1x1x1, 16

16 16

Block 2

1

1x1x1, 16

16

Output

8

1x1, 2

8

Softmax
Layer

1x1, 128

8

3x3, 64

4

3x3, 64

Block 5

4

3x3, 64

3x3, 64

Block 4

4

3x3, 64

3x3, 64

Block 3

2

3x3, 64

2

3x3, 64

1

Dropout
Layer

3x3, 64

Block 2

1

Concatenation
Layer

3x3, 64

Block 1

3x3, 64

Input Image

Convolutional
Layer with ReLU

Block 1

3x3x3, 64

Fig. 1. The proposed interactive segmentation framework (BIFSeg). 2D images are shown as examples. In the training stage, each instance is cropped with its
bounding box, and the CNN model is trained for binary segmentation. In the testing stage, image-specific fine-tuning with optional scribbles and a weighted
loss function is used. Note that the object class (e.g. a maternal kidney) in the test image may have not been present in the training set.

(b) PC-Net for segmentation of a 3D volume

Fig. 2. Our resolution-preserving networks with dilated convolution for 2D segmentation (a) and 3D segmentation (b). The numbers in each dark blue box
denote convolutional kernel size and number of output channels, and the number on the top denotes dilation parameter.

blocks and 3×3×1 kernels in block3 to block5. Similar to
P-Net, we fine-tune the classifier (block6) with pre-computed
concatenated features. To save space for storing the concatenated features, we use 1×1×1 convolutions to compress the
features in block1 to block5 and then concatenate them. We
refer to this 3D network with feature compression as PC-Net.
B. Training of CNNs
The training stage for 2D/3D segmentation is shown in the
first row of Fig. 1. Consider a K-ary segmentation training set
T = {(X1 , Y1 ), (X2 , Y2 ), ...} where Xp is one training image
and Yp is the corresponding label map. The label set of T is
{0, 1, 2, ..., K − 1} with 0 being the background label. Let Nk
denote the number of instances ofPthe kth object type, so the
total number of instances is N̂ = k Nk . Each image Xp can
have instances of multiple object classes. Suppose the label of
the qth instance in Xp is lpq , Yp is converted into a binary
image Ypq based on whether the value of each pixel in Yp
equals to lpq . The bounding box Bpq of that training instance
is automatically calculated based on Ypq and expanded by a
random margin in the range of 0 to 10 pixels/voxels. Xp and

Ypq are cropped based on Bpq . Thus, T is converted into a
cropped set T̂ = {(X̂1 , Ŷ1 ), (X̂2 , Ŷ2 ), ...} with size N̂ and
label set {0, 1} where 1 is the label of the instance foreground
and 0 the background. With T̂ , the CNN model (e.g, P-Net or
PC-Net) is trained to extract the target from its bounding box,
which is a binary segmentation problem irrespective of the
object type. A cross entropy loss function is used for training.

C. Unsupervised and Supervised Image-specific Fine-tuning
In the testing stage, let X̂ denote the sub-image inside a
user-provided bounding box and Ŷ be the target label of X̂.
The set of parameters of the trained CNN is θ. With the initial
segmentation Ŷ0 obtained by the trained CNN, the user may
provide (i.e., supervised) or not provide (i.e., unsupervised) a
set of scribbles to guide the update of Ŷ0 . Let S f and S b denote
the scribbles for foreground and background, respectively, so
the entire set of scribbles is S = S f ∪ S b . Let si denote the
user-provided label of a pixel in the scribbles, then we have
si = 1 if i ∈ Sf and si = 0 if i ∈ Sb . We minimize an

4

objective function that is similar to GrabCut [20] but we use
P-Net or PC-Net instead of a GMM:




X
X
φ(ŷi |X̂, θ) + λ
ψ(ŷi , ŷj |X̂)
arg min E(Ŷ , θ) =


Ŷ ,θ
i

subject to : ŷi = si

i,j

θ

if i ∈ S

(1)
where E(Ŷ , θ) is constrained by user interactions if S is
not empty. φ and ψ are the unary and pairwise energy
terms, respectively. λ is the weight of ψ. An unconstrained
optimization of an energy similar to E is used in [5] for weakly
supervised learning. In that work, the energy was based on the
probability and label map of all the images in a training set,
which is a different task from ours, as we focus on a single
test image. We follow a typical choice of ψ [19]:
!
(X̂(i) − X̂(j))2
1
ψ(ŷi , ŷj |X̂) = [ŷi 6= ŷj ]exp −
·
(2)
2σ 2
dij
where [·] is 1 if ŷi 6= ŷj and 0 otherwise. dij is the Euclidean
distance between pixel i and pixel j. σ controls the effect of
intensity difference. φ is defined as:
φ(ŷi |X̂, θ) = −logP (ŷi |X̂, θ)

(3)

where P (ŷi |X̂, θ) is the probability given by softmax output
of the CNN. Let pi = P (ŷi = 1|X̂, θ) be the probability of
pixel i belonging to the foreground, we then have:
logP (ŷi |X̂, θ) = ŷi logpi + (1 − ŷi )log(1 − pi )

(4)

The optimization of Eq. (1) can be decomposed into steps
that alternatively update the segmentation label Ŷ and network
parameters θ [5], [20]. In the label update step, we fix θ and
solve for Ŷ , and Eq. (1) becomes a CRF problem:




X
X
arg min E(θ) =
φ(ŷi |X̂, θ) + λ
ψ(ŷi , ŷj |X̂)

 (5)
Ŷ
i
i,j
subject to : ŷi = si

if i ∈ S

For implementation ease, the constrained optimization in
Eq. (5) is converted to an unconstrained equivalent:


X

X
arg min
φ0 (ŷi |X̂, θ) + λ
ψ(ŷi , ŷj |X̂)
(6)


Ŷ
i



+∞
0
φ (ŷi |X̂, θ) = 0


−logP (ŷi |X̂, θ)

i,j

if i ∈ S and ŷi = si
if i ∈ S and ŷi =
6 si
otherwise
(7)

Since θ and therefore φ0 are fixed, and ψ is submodular, Eq. (6)
can be solved by Graph Cuts [19]. In the network update step,
we fix Ŷ and solve for θ:
(
)
X
arg min E(Ŷ ) =
φ(ŷi |X̂, θ)
(8)
θ
i
subject to : ŷi = si

Thanks to the constrained optimization in Eq. (5), the label
update step necessarily leads to ŷi = si for i ∈ S. Eq. (8) can
be treated as an unconstrained optimization:
(
)

X
ŷi logpi + (1 − ŷi )log(1 − pi )
arg min −
(9)

if i ∈ S

i

D. Weighted Loss Function during Network Update Step
During the network update step, the CNN is fine-tuned to
fit the current segmentation Ŷ . Compared with a standard
learning process that treats all the pixels equally, we propose
to weight different kind of pixels considering their confidence.
First, user-provided scribbles have much higher confidence
than the other pixels, and they should have a higher impact
on the loss function, leading to a weighted version of Eq. (3):
φ(ŷi |X̂, θ) = −w(i)logP (ŷi |X̂, θ)
(
ω
w(i) =
1

if i ∈ S
otherwise

(10)

(11)

where ω ≥ 1 is the weight associated with scribbles. φ defined
in Eq. (10) allows Eq. (5) to remain unchanged for the label
update step. In the network update step, Eq. (9) becomes:
)
(


X
arg min −
w(i) ŷi logpi + (1 − ŷi )log(1 − pi )
θ

i

(12)
Note that the energy optimization problem of Eq. (1) remains
well-posed with Eq. (10), (11), and (12).
Second, Ŷ may contain mis-classified pixels that can mislead the network update process. To address this problem, we
propose to fine-tune the network by ignoring pixels with high
uncertainty (low confidence) in the test image. We propose to
use network-based uncertainty and scribble-based uncertainty.
The network-based uncertainty is based on the network’s softmax output. Since ŷi is highly uncertain (has low confidence)
if pi is close to 0.5, we define the set of pixels with high
network-based uncertainty as Up = {i|t0 < pi < t1 } where t0
and t1 are the lower and higher threshold values of foreground
probability, respectively. The scribble-based uncertainty is
based on the geodesic distance to scribbles. Let G(i, S f ) and
G(i, S b ) denote the geodesic distance [4] from pixel i to S f
and S b , respectively. Since the scribbles are drawn on missegmented areas for refinement, it is likely that pixels close to
S have been incorrectly labeled by the initial segmentation.
Let  be a threshold value for the geodesic distance. We
define the set of pixels with high scribble-based uncertainty as
Us = Usf ∪ Usb where Usf = {i|i ∈
/ S, G(i, S f ) < , yˆi = 0},
b
b
Us = {i|i ∈
/ S, G(i, S ) < , yˆi = 1}. Therefore, a full version
of the weighting function is (an example is shown in Fig. 3):


if i ∈ S
ω
w(i) = 0
(13)
if i ∈ Up ∪ Us


1
otherwise
The new definition of w(i) is well motivated in the network
update step. However, in the label update step, introducing

5

User-provided
bounding box

Initial segmentation

Foreground scribble

User-provided bounding box

Background scribble

Segmentation result

Ground truth

Input

Input

Foreground probability Binary output

Scribbles

Weight map

Fig. 3. An example of weight map for image-specific fine-tuning. The weight
is 0 for pixels with high uncertainty (black), ω for scribbles (white), and 1
for the remaining pixels (gray).
TABLE I
Q UANTITATIVE COMPARISON OF INITIAL FETAL MRI SEGMENTATION
FROM A BOUNDING BOX . Tm IS THE MACHINE TIME . ∧ DENOTES
PREVIOUSLY UNSEEN OBJECTS . I N EACH ROW, BOLD FONT DENOTES THE
BEST VALUE . * DENOTES p- VALUE < 0.05 COMPARED WITH THE OTHERS .
FCN
P
85.31±8.73
Dice FB
89.53±3.91
(%) FL∧ 81.68±5.95
MK∧ 83.58±5.48
Tm (s)
0.11±0.04*
P: Placenta, FB: Fetal brain,

U-Net
P-Net
82.86±9.85
84.57±8.37
89.19±5.09
89.44±6.45
80.64±6.10
83.59±6.42*
75.20±11.23 85.29±5.08*
0.24±0.07
0.16±0.05
FL: Fetal lungs, MK: Maternal

GrabCut
62.90±12.79
83.86±14.33
63.99±15.86
73.85±7.77
1.62±0.42
kidneys.

zero unary weights in Eq. (5) would make the label update of
corresponding pixels entirely driven by the pairwise potentials.
Therefore, we choose to keep Eq. (5) unchanged.
E. Implementation Details
We used the Caffe1 [27] library to implement our P-Net and
PC-Net. The training process was done via one node of the
Emerald cluster2 with two 8-core E5-2623v3 Intel Haswells, a
K80 NVIDIA GPU and 128GB memory. Stochastic gradient
decent was used for training, with momentum 0.9, batch size
1, weight decay 5 × 10−4 , maximal number of iterations 60k,
initial learning 10−3 that was halved every 5k iterations. For
each application, the images in each modality were normalized
by the mean value and standard variation of the training
images. During training, the bounding box for each object
was automatically generated based on the ground truth label
with a random margin in the range of 0 to 10 pixels/voxels.
For the testing with user interactions, the trained CNN
models were deployed to a MacBook Pro (OS X 10.9.5) with
16GB RAM, an Intel Core i7 CPU running at 2.5GHz and an
NVIDIA GeForce GT 750M GPU. A Matlab GUI and a PyQt
GUI were used for user interactions on 2D and 3D images,
respectively. The bounding box was provided by the user. For
image-specific fine-tuning, Ŷ and θ were alternatively updated
for four iterations. In each network update step, we used a
learning rate 10−2 and iteration number 20. We used a grid
search with the training data to get proper values of λ, σ, t0 ,
t1 ,  and ω. Their numerical values are listed in the specific
experiments sections III-B and III-C.
III. E XPERIMENTS AND R ESULTS
We validated the proposed framework with two applications:
2D segmentation of multiple organs from fetal MRI and

GrabCut

FCN

U-Net

P-Net

Placenta

Fetal brain

Previously seen

Fetal lungs

Maternal kidneys

Previously unseen

Fig. 4. Visual comparison of initial segmentation of multiple organs from
fetal MRI with a bounding box. All the methods use the same bounding
box for each test instance. Note that fetal lungs and maternal kidneys are
previously unseen objects but P-Net works well on them.

3D segmentation of brain tumors from contrast enhanced
T1-weighted (T1c) and Fluid-attenuated Inversion Recovery
(FLAIR) images. For both applications, we additionally investigated the segmentation performance on previously unseen
objects that were not present in the training set.
A. Comparison Methods and Evaluation Metrics
To investigate the performance of different networks with
the same bounding box, we compared P-Net with FCN [11]
and U-Net [13] for 2D images, and compared PC-Net with
DeepMedic [9] and HighRes3DNet [10] for 3D images3 . The
original DeepMedic works on multiple modalities, and we
adapted it to work on a single modality. All these methods
were evaluated on the laptop during the testing except for
HighRes3DNet that was run on the cluster due to the laptop’s limited GPU memory. To validate the proposed unsupervised/supervised image-specific fine-tuning, we compared
BIFSeg with 1) the initial output of P-Net/PC-Net, 2) postprocessing the initial output with a CRF (using user interactions as hard constraints if they were given), and 3) imagespecific fine-tuning based on Eq. (1) with w(i) = 1 for all the
pixels, which is referred to as BIFSeg(-w).
BIFSeg was also compared with other interactive segmentation methods: GrabCut [20], SlicSeg [21] and Random
Walks [3] for 2D segmentation, and GeoS [4], GrowCut [28]
and 3D GrabCut [29] for 3D segmentation. The 2D/3D
GrabCut used the same bounding box as used by BIFSeg,
and they used 3 and 5 components for the foreground and
background GMMs, respectively. SlicSeg, Random Walks,

1 http://caffe.berkeleyvision.org
2 http://www.ses.ac.uk/high-performance-computing/emerald

3 DeepMedic

and HighRes3DNet were implemented in http://niftynet.io

6

User-provided bounding box

Segmentation result

Ground truth

Input

P-Net

TABLE II
Q UANTITATIVE COMPARISON OF P-N ET AND THREE UNSUPERVISED
REFINEMENT METHODS FOR FETAL MRI SEGMENTATION . Tm IS THE
MACHINE TIME FOR REFINEMENT. ∧ DENOTES PREVIOUSLY UNSEEN
OBJECTS . I N EACH ROW, BOLD FONT DENOTES THE BEST VALUE . *
DENOTES p- VALUE < 0.05 COMPARED WITH THE OTHERS .

Foreground probability

P-Net
P-Net+CRF
BIFSeg(-w)
BIFSeg
P
84.57±8.37 84.87±8.14
82.74±10.91 86.41±7.50*
Dice FB
89.44±6.45 89.55±6.52
89.09±8.08
90.39±6.44
(%) FL∧ 83.59±6.42 83.87±6.52
82.17±8.87
85.35±5.88*
MK∧ 85.29±5.08 85.45±5.21
84.61±6.21
86.33±4.28*
Tm (s)
0.02±0.01*
0.71±0.12
0.72±0.12
P: Placenta, FB: Fetal brain, FL: Fetal lungs, MK: Maternal kidneys.

BIFSeg
(-w)

BIFSeg

P-Net

Segmentation

P-Net +
CRF

BIFSeg
(-w)

BIFSeg

Placenta

Fetal brain

Previously seen

Fetal lungs Maternal kidneys

Previously unseen

Fig. 5.
Visual comparison of P-Net and three unsupervised refinement
methods for fetal MRI segmentation. The foreground probability is visualized
by heatmap.

GeoS and GrowCut require scribbles without a bounding box
for segmentation. The segmentation results by an Obstetrician
and a Radiologist were used for evaluation. For each method,
each user provided scribbles to update the result multiple times
until the user accepted it as the final segmentation. The Dice
score between a segmentation and the ground truth was used
for quantitative evaluations: Dice = 2|Ra ∩ Rb |/(|Ra |+|Rb |)
where Ra and Rb denote the region segmented by an algorithm and the ground truth, respectively. The p-value between
different methods was computed by the Student’s t-test.
B. 2D Segmentation of Multiple Organs from Fetal MRI
1) Data: Single-shot Fast Spin Echo (SSFSE) was used to
acquire stacks of T2-weighted MR images from 18 pregnant
women with pixel size 0.74 to 1.58 mm and inter-slice spacing
3 to 4 mm. Due to the large inter-slice spacing and interslice motion, interactive 2D segmentation is more suitable than
direct 3D segmentation [21]. The placenta and fetal brain from
10 volumes (356 slices) were used for training. The other
8 volumes (318 slices) were used for testing. From the test
images, we aimed to segment the placenta, fetal brain, and
previously unseen fetal lungs and maternal kidneys. Manual
segmentations by a Radiologist were used as the ground truth.
P-Net was used for this segmentation task. To deal with organs

at different scales, we resized the input of P-Net so that the
minimal value of width and height was 128 pixels. Parameter
setting was λ = 3.0, σ = 0.1, t0 = 0.2, t1 = 0.7,  = 0.2, ω =
5.0 based on a grid search with the training data.
2) Initial Segmentation based on P-Net: Fig. 4 shows the
initial segmentation of different organs from fetal MRI with
user-provided bounding boxes. It can be observed that GrabCut
achieves a poor segmentation except for the fetal brain where
there is a good contrast between the target and the background.
For the placenta and fetal brain, FCN, U-Net and P-Net
achieves visually similar results that are close to the ground
truth. However, for fetal lungs and maternal kidneys that are
previously unseen in the training set, FCN and U-Net lead
to a large region of under-segmentation. In contrast, P-Net
performs noticeably better than FCN and U-Net when dealing
with these two unseen objects. A quantitative evaluation of
these methods are listed in Table I. It shows that P-Net
achieves the best accuracy for unseen fetal lungs and maternal
kidneys with average machine time 0.16s.
3) Unsupervised Image-specific Fine-tuning: For unsupervised refinement, the initial segmentation result obtained by
P-Net was refined by CRF, BIFSeg(-w) and BIFSeg without
additional scribbles, respectively. The results are shown in
Fig. 5. The second to fourth rows show the foreground
probability obtained by P-Net before and after the fine-tuning.
In the second row, the initial output of P-Net has a probability
around 0.5 for many pixels, which indicates a high uncertainty.
After image-specific fine-tuning, most pixels in the outputs of
BIFSeg(-w) and BIFSeg have a probability close to 0.0 or 1.0.
The remaining rows show the segmentations by P-Net and the
three refinement methods, respectively. The visual comparison shows that BIFSeg performs better than P-Net + CRF
and BIFSeg(-w). Quantitative measurements are presented in
Table II. It shows that BIFSeg achieves a larger improvement
of accuracy from the initial segmentation when compared with
the use of CRF or BIFSeg(-w). In this 2D case, BIFSeg takes
0.72s in average for unsupervised image-specific fine-tuning.
4) Supervised Image-specific Fine-tuning: Fig. 6 shows
examples of supervised refinement with additional scribbles.
The second row shows the initial segmentation obtained by
P-Net. In the third row, red and blue scribbles are drawn
in mis-segmented regions to label the corresponding pixels
as the foreground and background, respectively. The same
initial segmentation and scribbles are used for P-Net + CRF,
BIFSeg(-w) and BIFSeg. All these methods improve the seg-

7

User-provided
bounding box

Foreground
scribble

Background
scribble

Segmentation
result

Ground
truth

Input

TABLE III
Q UANTITATIVE COMPARISON OF P-N ET AND THREE SUPERVISED
REFINEMENT METHODS WITH SCRIBBLES FOR FETAL MRI
SEGMENTATION . Tm IS THE MACHINE TIME FOR REFINEMENT. ∧ DENOTES
PREVIOUSLY UNSEEN OBJECTS . I N EACH ROW, BOLD FONT DENOTES THE
BEST VALUE . * DENOTES p- VALUE < 0.05 COMPARED WITH THE OTHERS .
P-Net
P
84.57±8.37
Dice FB
89.44±6.45
(%) FL∧ 83.59±6.42
MK∧ 85.29±5.08
Tm (s)
P: Placenta, FB: Fetal brain,

P-Net

Scribbles

P-Net+CRF
BIFSeg(-w)
88.64±5.84
89.79±4.60
94.04±4.72
95.31±3.39
88.92±3.87
89.21±2.95
87.51±4.53
87.78±4.46
0.02±0.01*
0.72±0.11
FL: Fetal lungs, MK: Maternal

User-provided bounding box

P-Net +
CRF

BIFSeg
91.93±2.79*
95.58±1.94
91.71±3.18*
89.37±2.31*
0.74±0.12
kidneys.

Segmentation result

Ground truth

Axial
(a)
Tumor
core in
T1c

Sagittal

BIFSeg
(-w)

(previously
seen)

Coronal

BIFSeg

Placenta

Fetal brain

Fetal lungs

Maternal kidneys
Previously unseen

Fig. 6. Visual comparison of P-Net and three supervised refinement methods
for fetal MRI segmentation. The same initial segmentation and scribbles are
used for P-Net + CRF, BIFSeg(-w) and BIFSeg.

Axial

Previously seen

(previously
unseen)

Sagittal

(b)
Whole
tumor in
FLAIR

Coronal
Input

3D GrabCut

DeepMedic

HighRes3DNet

PC-Net

Fig. 8. Visual comparison of initial segmentation of brain tumors from a 3D
bounding box. The whole tumor in FLAIR is previously unseen in the training
set. All these methods use the same bounding box for each test image.

Fig. 7. User time and Dice score of different interactive methods for fetal
MRI segmentation. ∧ denotes previously unseen objects for BIFSeg.

mentation. However, some large mis-segmentations can still
be observed for P-Net + CRF and BIFSeg(-w). In contrast,
BIFSeg achieves better results with the same set of scribbles.
For a quantitative comparison, we measured the segmentation
accuracy after a single round of refinement using the same
set of scribbles. The result is shown in Table III. BIFSeg
achieves significantly better accuracy (p-value < 0.05) for
the placenta, and previously unseen fetal lungs and maternal
kidneys compared with P-Net + CRF and BIFSeg(-w).
5) Comparison with Other Interactive Methods: The two
users (an Obstetrician and a Radiologist) used SlicSeg [21],
GrabCut [20], Random Walks [3] and BIFSeg for the fetal
MRI segmentation tasks respectively. For each image, the user

implemented the segmentation interactively until the result was
accepted by the user. The user time and final accuracy of are
presented in Fig. 7. It shows that BIFSeg takes noticeably less
user time with similar or higher accuracy compared with the
other three interactive segmentation methods.

TABLE IV
D ICE SCORE OF INITIAL SEGMENTATION OF BRAIN TUMORS FROM A 3D
BOUNDING BOX . A LL THE METHODS USE THE SAME BOUNDING BOX FOR
EACH TEST IMAGE . ∧ DENOTES PREVIOUSLY UNSEEN OBJECTS . I N EACH
ROW, BOLD FONT DENOTES THE BEST VALUE . * DENOTES p- VALUE < 0.05
COMPARED WITH THE OTHERS .
DeepMedic
TC
76.68±11.83
WT∧
84.04±8.50
TC: Tumor core in T1c,

HighRes3DNet
83.45±7.87
75.60±8.97
WT: Whole tumor

PC-Net
82.66±7.78
83.52±8.76
in FLAIR.

3D GrabCut
69.24±19.20
78.39±18.66

8

User-provided bounding box

Segmentation result

Ground truth

Axial
(previously
seen)

Sagittal

(a) Tumor
core in T1c

Coronal
Axial
(previously
unseen)

Sagittal

(b) Whole
tumor in
FLAIR

Coronal
Input

PC-Net

BIFSeg(-w)
Foreground probability

BIFSeg

PC-Net

PC-Net + CRF

BIFSeg(-w)

BIFSeg

Segmentation

Fig. 9. Visual comparison of PC-Net and unsupervised refinement methods without additional scribbles for 3D brain tumor segmentation. The same initial
segmentation obtained by PC-Net is used by different refinement methods.

TABLE V
Q UANTITATIVE COMPARISON OF PC-N ET AND UNSUPERVISED
REFINEMENT METHODS WITHOUT ADDITIONAL SCRIBBLES FOR 3D BRAIN
TUMOR SEGMENTATION . Tm IS THE MACHINE TIME FOR REFINEMENT. ∧
DENOTES PREVIOUSLY UNSEEN OBJECTS . I N EACH ROW, BOLD FONT
DENOTES THE BEST VALUE . * DENOTES p- VALUE < 0.05 COMPARED WITH
THE OTHERS .
PC-Net
PC-Net+CRF
BIFSeg(-w)
TC
82.66±7.78 84.33±7.32
84.67±7.44
WT∧ 83.52±8.76 83.92±7.33
83.88±8.62
TC
0.12±0.04*
3.36±0.82
Tm (s)
WT∧ 0.11±0.05*
3.16±0.89
TC: Tumor core in T1c, WT: Whole tumor in FLAIR.
Dice
(%)

BIFSeg
86.13±6.86*
86.29±7.31*
3.32±0.82
3.09±0.83

C. 3D Segmentation of Brain Tumors from T1c and FLAIR
1) Data: To validate our method with 3D images, we
used the 2015 Brain Tumor Segmentation Challenge (BRATS)
training set [30]. The ground truth were manually delineated
by experts. This dataset was collected from 274 cases with
multiple MR sequences that give different contrasts. T1c
highlights the tumor without peritumoral edema, designated
“tumor core” as per [30]. FLAIR highlights the tumor with
peritumoral edema, designated “whole tumor” as per [30].
We investigate interactive segmentation of tumor cores from
T1c images and whole tumors from FLAIR images, which
is different from previous works on automatic multi-label
and multi-modality segmentation [9], [31]. For tumor core
segmentation, we randomly selected 249 T1c volumes as
our training set and used the remaining 25 T1c volumes

as the testing set. Additionally, to investigate dealing with
unseen objects, we employed such trained CNNs to segment
whole tumors in the corresponding FLAIR images of these 25
volumes that were not present in our training set. All these
images had been skull-stripped and resampled to isotropic
1mm3 resolution. To deal with 3D tumor cores and whole
tumors at different scales, we resized the cropped image region
inside a bounding box to make its maximal value of width,
height and depth be 80. Parameter setting was λ = 10.0, σ =
0.1, t0 = 0.2, t1 = 0.6,  = 0.2, ω = 5.0 based on a grid search
with the training data.
2) Initial Segmentation based on PC-Net: Fig. 8(a) shows
an initial result of tumor core segmentation from T1c with a
user-provided bounding box. Since the central region of the
tumor has a low intensity close to that of the background, 3D
GrabCut has a poor performance with under-segmentations.
DeepMedic leads to some over-segmentations. HighRes3DNet
and PC-Net obtain similar results, but PC-Net is less complex
and has a lower memory consumption. Fig. 8(b) shows an
initial segmentation result of previously unseen whole tumor
from FLAIR. 3D GrabCut fails to get high accuracy due to
intensity inconsistency in the tumor region, and the CNNs
outperform 3D GrabCut, with DeepMedic and PC-Net performing better than HighRes3DNet. A quantitative comparison
is presented in Table IV. It shows that the performance of
DeepMedic is low for T1c but high for FLAIR, and that of
HighRes3DNet is the opposite. This is because DeepMedic
has a small receptive field and tends to rely on local features.

9

User-provided
bounding box

Foreground
scribble

Background
scribble

Segmentation result

Ground truth

Axial
(previously
seen)

Sagittal

(a) Tumor
core in T1c

TABLE VI
Q UANTITATIVE COMPARISON OF PC-N ET AND THREE SUPERVISED
REFINEMENT METHODS WITH ADDITIONAL SCRIBBLES FOR 3D BRAIN
TUMOR SEGMENTATION . Tm IS THE MACHINE TIME FOR REFINEMENT. ∧
DENOTES PREVIOUSLY UNSEEN OBJECTS . I N EACH ROW, BOLD FONT
DENOTES THE BEST VALUE . * DENOTES p- VALUE < 0.05 COMPARED WITH
THE OTHERS .
PC-Net
PC-Net+CRF
BIFSeg(-w)
TC
82.66±7.78 85.93±6.64
85.88±7.53
WT∧ 83.52±8.76 85.18±6.78
86.54±7.49
TC
0.14±0.06*
3.33±0.86
Tm (s)
WT∧ 0.12±0.05*
3.17±0.87
TC: Tumor core in T1c, WT: Whole tumor in FLAIR.
Dice
(%)

Coronal

BIFSeg
87.49±6.36*
88.11±6.09*
4.42±1.88
4.01±1.59

Axial
(previously
unseen)

Sagittal

(b) Whole
tumor in
FLAIR

Coronal
Input

PC-Net

Scribbles

PC-Net + CRF

BIFSeg (-w)

BIFSeg

Fig. 10.
Visual comparison of PC-Net and three supervised refinement
methods with scribbles for 3D brain tumor segmentation. The refinement
methods use the same initial segmentation and set of scribbles.

It is difficult to use local features to deal with T1c due to
its complex appearance but easier to deal with FLAIR since
the appearance is less complex. HighRes3DNet has a more
complex model and tends to over-fit tumor core. In contrast,
PC-Net achieves a more stable performance on tumor core and
previously unseen whole tumor. The average machine time
for 3D GrabCut, DeepMedic, and PC-Net is 3.87s, 65.31s and
3.83s, respectively (on the laptop), and that for HighRes3DNet
is 1.10s (on the cluster).
3) Unsupervised Image-specific Fine-tuning: Fig. 9 shows
unsupervised fine-tuning for brain tumor segmentation based
on the initial output of PC-Net without additional user interactions. In Fig. 9(a), the tumor core is under-segmented in
the initial output of PC-Net. CRF improves the segmentation
to some degree, but large areas of under-segmentation still
exist. The segmentation result of BIFSeg(-w) is similar to that
of CRF. In contrast, BIFSeg performs better than CRF and
BIFSeg(-w). A similar situation is observed in Fig. 9(b) for
segmentation of previously unseen whole tumor. A quantitative
comparison of these methods is shown in Table V. BIFSeg
improves the average dice score from 82.66% to 86.13% for
tumor core, and from 83.52% to 86.29% for whole tumor.
4) Supervised Image-specific Fine-tuning: Fig 10 shows
refined results of brain tumor segmentation with additional
scribbles provided by the user. The same initial segmentation
based on PC-Net and the same scribbles are used by CRF,
BIFSeg(-w) and BIFSeg. It can be observed that CRF and
BIFSeg(-w) correct the initial segmentation moderately. In
contrast, BIFSeg achieves better refined results for both tumor
cores in T1c and whole tumors in FLAIR. For a quantitative

Fig. 11. User time and Dice score of different interactive methods for 3D
brain tumor segmentation. ∧ denotes previously unseen objects for BIFSeg.

comparison of these refinement methods, we measured the
segmentation accuracy after a single round of refinement using
the same set of scribbles based on the same initial segmentation. The result is shown in Table VI. BIFSeg achieves
an average dice score of 87.49% and 88.11% for tumor
core and previously unseen whole tumor, respectively, and it
significantly outperforms CRF and BIFSeg(-w).
5) Comparison with Other Interactive Methods: The two
users (an Obstetrician and a Radiologist) used GeoS [4],
GrowCut [28], 3D GrabCut [29] and BIFSeg for the brain
tumor segmentation tasks respectively. The user time and final
accuracy of these methods are presented in Fig. 11. It shows
that these interactive methods achieve similar final Dice scores
for each task. However, BIFSeg takes significantly less user
time to get the results, which is 82.3s and 68.0s in average
for tumor core and whole tumor, respectively.
IV. D ISCUSSION AND C ONCLUSION
For 2D images, our P-Net is trained with placenta and fetal
brain only, but it performs well on previously unseen fetal
lungs and maternal kidneys. For 3D images, the PC-Net is
only trained with tumor cores in T1c, but it also achieves good
results for whole tumors in FLAIR that are not present for
training. This is a major advantage compared with traditional
CNNs and even transfer learning [25] or weakly supervised
learning [5], since for some objects it does not require annotated instances for training at all. It therefore reduces the
efforts needed for gathering and annotating training data and
can be applied to some unseen organs directly. Our proposed
framework accepts bounding boxes and optional scribbles as
user interactions. Bounding boxes in test images are provided
by the user, but they could potentially be obtained by automatic
detection [32] to further increase efficiency. Experimental

10

results show that the image-specific fine-tuning improves the
segmentation performance. This acts as a post-processing step
after the initial segmentation and outperforms CRF. We found
that taking advantage of uncertainty plays an important role
for the image-specific fine-tuning process. The uncertainty is
defined based on softmax probability and geodesic distance
to scribbles if scribbles are given. Recent works [33] suggest
that test-time dropout also provides classification uncertainty.
However, test-time dropout is less suited for interactive segmentation since it leads to longer computational time.
In conclusion, we propose an efficient deep learning-based
framework for interactive 2D/3D medical image segmentation.
It uses a bounding box-based CNN for binary segmentation and can segment previously unseen objects. A unified
framework is proposed for both unsupervised and supervised
refinements of the initial segmentation, where image-specific
fine-tuning based on a weighted loss function is proposed.
Experiments on segmenting multiple organs from 2D fetal
MRI and brain tumors from 3D MRI show that our method
performs well on previously unseen objects and the imagespecific fine-tuning outperforms CRF. BIFSeg achieves similar
or higher accuracy with fewer user interactions in less time
than traditional interactive segmentation methods.
ACKNOWLEDGMENT
This work was supported by the Wellcome Trust
(WT101957, WT97914, HICF-T4-275), the EPSRC
(NS/A000027/1, EP/H046410/1, EP/J020990/1, EP/K005278,
NS/A000050/1), Wellcome/EPSRC [203145Z/16/Z], the
Royal Society [RG160569], the National Institute for Health
Research University College London Hospitals Biomedical
Research Centre (NIHR BRC UCLH/UCL), a UCL ORS
and GRS, hardware donated by NVIDIA, and by Emerald, a
GPU-accelerated High Performance Computer, made available
by the Science & Engineering South Consortium operated in
partnership with the STFC Rutherford-Appleton Laboratory.
R EFERENCES
[1] G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi,
M. Ghafoorian, J. A. van der Laak, B. V. Ginneken, and C. I. Sánchez,
“A survey on deep learning in medical image analysis,” Medical Image
Analysis, vol. 42, pp. 60–88, 2017.
[2] F. Zhao and X. Xie, “An overview of interactive medical image
segmentation,” Annals of the BMVA, vol. 2013, no. 7, pp. 1–22, 2013.
[3] L. Grady, T. Schiwietz, S. Aharon, and R. Westermann, “Random
walks for interactive organ segmentation in two and three dimensions:
implementation and validation,” in MICCAI, 2005, pp. 773–780.
[4] A. Criminisi, T. Sharp, and A. Blake, “GeoS: Geodesic image segmentation,” in ECCV, 2008, pp. 99–112.
[5] M. Rajchl, M. Lee, O. Oktay, K. Kamnitsas, J. Passerat-Palmbach,
W. Bai, M. Rutherford, J. Hajnal, B. Kainz, and D. Rueckert, “DeepCut:
Object segmentation from bounding box annotations using convolutional
neural networks,” TMI, vol. 36, no. 2, pp. 674–683, 2017.
[6] N. Xu, B. Price, S. Cohen, J. Yang, and T. Huang, “Deep interactive
object selection,” in CVPR, 2016, pp. 373–381.
[7] G. Wang, M. A. Zuluaga, W. Li, R. Pratt, P. A. Patel, M. Aertsen,
T. Doel, M. Klusmann, A. L. David, J. Deprest, S. Ourselin, and
T. Vercauteren, “DeepIGeoS: A deep interactive geodesic framework for
medical image segmentation,” arXiv preprint arXiv:1707.00652, 2017.
[8] H. L. Ribeiro and A. Gonzaga, “Hand image segmentation in video
sequence by GMM: a comprarative analysis,” in SIBGRAPI, 2006, pp.
357–364.

[9] K. Kamnitsas, C. Ledig, V. F. J. Newcombe, J. P. Simpson, A. D. Kane,
D. K. Menon, D. Rueckert, and B. Glocker, “Efficient multi-scale 3D
CNN with fully connected CRF for accurate brain lesion segmentation,”
Medical Image Analysis, vol. 36, pp. 61–78, 2017.
[10] W. Li, G. Wang, L. Fidon, S. Ourselin, M. J. Cardoso, and T. Vercauteren, “On the compactness, efficiency, and representation of 3D
convolutional networks: brain parcellation as a pretext task,” in IPMI,
2017, pp. 348–360.
[11] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks
for semantic segmentation,” in CVPR, 2015, pp. 3431–3440.
[12] L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille,
“Semantic image segmentation with deep convolutional nets and fully
connected CRFs,” in ICLR, 2015.
[13] O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in MICCAI, 2015, pp. 234–
241.
[14] H. Chen, X. Qi, L. Yu, and P.-A. Heng, “DCAN: Deep contour-aware
networks for accurate gland segmentation,” in CVPR, 2016, pp. 2487–
2496.
[15] R. Mckinley, R. Wepfer, T. Gundersen, F. Wagner, A. Chan, R. Wiest,
and M. Reyes, “Nabla-net: A deep dag-like convolutional architecture
for biomedical image segmentation,” in BrainLes, 2016, pp. 119–128.
[16] H. R. Roth, L. Lu, A. Farag, H.-c. Shin, J. Liu, E. B. Turkbey, and
R. M. Summers, “DeepOrgan: Multi-level deep convolutional networks
for automated pancreas segmentation,” in MICCAI, 2015, pp. 556–564.
[17] F. Milletari, N. Navab, and S.-A. Ahmadi, “V-Net: Fully convolutional
neural networks for volumetric medical image segmentation,” in IC3DV,
2016, pp. 565–571.
[18] Q. Dou, L. Yu, H. Chen, Y. Jin, X. Yang, J. Qin, and P.-A. Heng, “3D
deeply supervised network for automated segmentation of volumetric
medical images,” Medical Image Analysis, vol. 41, pp. 40–54, 2017.
[19] Y. Y. Boykov and M. P. Jolly, “Interactive graph cuts for optimal
boundary & region segmentation of objects in N-D images,” in ICCV,
2001, pp. 105–112.
[20] C. Rother, V. Kolmogorov, and A. Blake, “GrabCut: Interactive foreground extraction using iterated graph cuts,” ACM Trans. on Graphics,
vol. 23, no. 3, pp. 309–314, 2004.
[21] G. Wang, M. A. Zuluaga, R. Pratt, M. Aertsen, T. Doel, M. Klusmann,
A. L. David, J. Deprest, T. Vercauteren, and S. Ourselin, “Slic-Seg:
A minimally interactive segmentation of the placenta from sparse and
motion-corrupted fetal MRI in multiple views,” Medical Image Analysis,
vol. 34, pp. 137–147, 2016.
[22] A. Top, G. Hamarneh, and R. Abugharbieh, “Active learning for interactive 3D image segmentation,” in MICCAI, 2011, pp. 603–610.
[23] D. Lin, J. Dai, J. Jia, K. He, and J. Sun, “ScribbleSup: Scribblesupervised convolutional networks for semantic segmentation,” in CVPR,
2016, pp. 3159–3167.
[24] A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3D UNet : Learning dense volumetric segmentation from sparse annotation,”
in MICCAI, 2016, pp. 424–432.
[25] N. Tajbakhsh, J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall,
M. B. Gotway, and J. Liang, “Convolutional neural networks for medical
image analysis: full training or fine tuning?” TMI, vol. 35, no. 5, pp.
1299–1312, 2016.
[26] G. Wang, W. Li, S. Ourselin, and T. Vercauteren, “Automatic brain tumor
segmentation using cascaded anisotropic convolutional neural networks,”
arXiv preprint arXiv:1709.00382, 2017.
[27] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick,
S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for
fast feature embedding,” in ACMICM, 2014, pp. 675–678.
[28] V. Vezhnevets and V. Konouchine, “GrowCut: Interactive multi-label
ND image segmentation by cellular automata,” in Graphicon, 2005, pp.
150–156.
[29] E. Ram and P. Temoche, “A volume segmentation approach based on
GrabCut,” CLEI Electronic Journal, vol. 16, no. 2, pp. 4–4, 2013.
[30] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani,
J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, L. Lanczi, E. Gerstner, M. A. Weber, T. Arbel, B. B. Avants, N. Ayache, P. Buendia, D. L.
Collins, N. Cordier, J. J. Corso, A. Criminisi, T. Das, H. Delingette,
Ç. Demiralp, C. R. Durst, M. Dojat, S. Doyle, J. Festa, F. Forbes,
E. Geremia, B. Glocker, P. Golland, X. Guo, A. Hamamci, K. M.
Iftekharuddin, R. Jena, N. M. John, E. Konukoglu, D. Lashkari, J. A.
Mariz, R. Meier, S. Pereira, D. Precup, S. J. Price, T. R. Raviv, S. M.
Reza, M. Ryan, D. Sarikaya, L. Schwartz, H. C. Shin, J. Shotton, C. A.
Silva, N. Sousa, N. K. Subbanna, G. Szekely, T. J. Taylor, O. M.
Thomas, N. J. Tustison, G. Unal, F. Vasseur, M. Wintermark, D. H.
Ye, L. Zhao, B. Zhao, D. Zikic, M. Prastawa, M. Reyes, and K. Van

11

Leemput, “The multimodal brain tumor image segmentation benchmark
(BRATS),” TMI, vol. 34, no. 10, pp. 1993–2024, 2015.
[31] L. Fidon, W. Li, L. C. Garcia-Peraza-Herrera, J. Ekanayake, N. Kitchen,
S. Ourselin, and T. Vercauteren, “Scalable multimodal convolutional
networks for brain tumour segmentation,” in MICCAI, 2017, pp. 285–
293.
[32] K. Keraudren, M. Kuklisova-Murgasova, V. Kyriakopoulou, C. Malamateniou, M. A. Rutherford, B. Kainz, J. V. Hajnal, and D. Rueckert,
“Automated fetal brain segmentation from 2D MRI slices for motion
correction,” NeuroImage, vol. 101, pp. 633–643, 2014.
[33] Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation:
representing model uncertainty in deep learning,” in ICML, 2016, pp.
1050–1059.

