Automatic Segmentation of Necrosis Zones after Radiofrequency

Ablation of Spinal Metastases

Johannes Steffen

1,†

, Georg Hille

1,†

, Mathias Becker

, Sylvia Saalfeld

and Klaus T

onnies

Department of Simulation and Graphics, University of Magdeburg, Germany

Department of Neuroradiology, University Hospital of Magdeburg, Germany

†

both authors contributed equally to this manuscript

Keywords:

Medical Image Segmentation, Deep Learning, Radiofrequency Ablation, MRI.

Abstract:

In this work, we propose an automatic deep learning-based approach to segment necrotizing tissue (necrosis

zones) after radiofrequency ablations (RFA) of spinal metastases in follow-up Magnetic Resonance (MR)

images. While the manual segmentation of those necrosis zones is challenging and time consuming, it is

a crucial step to assess, whether a preceding therapy using RFA was successful and to what extent, i.e., to

quantitatively evaluate how much of the metastasis was necrotized throughout the therapy. Therefore, we

trained a U-Net like deep neural network on 26 clinical cases (and various augmentations of those), where

each case had an associated contrast enhanced T

-weighted as well as a T

-weighted MR sequence. We

evaluated the proposed approach on both sequences separately as well as in a combined setting and report

Dice coefﬁcients, sensitivity-, and speciﬁcity rates for the automatic segmentations. A Dice coefﬁcient of up

to 77.2 % indicates promising segmentation quality, if compared to related work and similar segmentation

tasks. To the best of our knowledge, this is the ﬁrst work to tackle the problem of automatic segmentation

of necrosis zones in MR images and therefore lacks comparability with related works. However, our best

results are somewhat superior to semi-automatic approaches of liver metastases segmentation, which might be

considered a problem of similar complexity.

1 INTRODUCTION

The life expectancy has increased steadily over the

last decades, resulting in a lifetime gain, which, in ad-

dition to all its beneﬁts, also holds risks. Age-related

diseases like cardiovascular diseases, as well as can-

cer and cancer induced malicious metastases occur

more and more frequently. Due to improved diagnos-

tic procedures and medical treatments, the survival

time of most malicious carcinomata has increased.

This medical success story unfortunately again has

a drawback, as the probability to develop metastases

raises. Bone metastases are the third most likely

and a vast majority are located in the spine (Harring-

ton, 1986; Wong et al., 1990). These spinal metas-

tases can evoke vigorous pain by fractures, bruises,

spinal cord and nerve root compressions or neurologic

deﬁcits and therefore, detrimentally affect the quality

of life (Klimo and Schmidt, 2004). Besides, external-

beam radiation the method of choice are ablative ap-

proaches like radiofrequency ablation (RFA) (Rosen-

thal et al., 1998; Dupuy et al., 2010). The underly-

ing princible is based on tissue heating and necro-

tization by molecular ﬁrction due to high frequency

current phase changes at the tip of a minimally inva-

sive inserted applicator. Heat dissipation leads to a

growing necrotized zone around the needle tip, with

the purpose of covering as much of the metastasis as

possible. MR images are taken for both diagnostic

purposes and post-interventional follow-ups, due to

their high soft tissue contrast and spatial resolution as

well as the possibility to utilize contrast agents. To

assess the treatment outcome, i.e. the completeness

of metastasis ablation it is essential to evaluate the

congruence of both the ablation zone and the former

metastasis via distance and volume overlap measures.

Therefore, the segmentation of the necrosis zone is

among others a pivotal step towards an automatised,

quantitative and objective treatment outcome valida-

tion of RFAs of spinal metastases. Besides, such seg-

mentations could yield additional insights for a bet-

ter understanding in tumor reoccurrence and to fur-

ther improve ablation protocols and procedures. Cur-

rently, it is not part of the clinical routine, due to

the very time-consuming manually contouring done

slice-wise, which makes it hardly applicable or suit-

able. Furthermore, some speciﬁc aspects of this issue

make this a highly challenging and ambitious task,

e.g. hardly noticeable intensity differences between

necrosis and remaining metastasis, inﬂammation pro-

Steffen, J., Hille, G., Becker, M., Saalfeld, S. and Tönnies, K.

Automatic Segmentation of Necrosis Zones after Radiofrequency Ablation of Spinal Metastases.

DOI: 10.5220/0009174300960102

In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020), pages 96-102

ISBN: 978-989-758-397-1; ISSN: 2184-4313

Figure 1: Original T

-weighted pre- (upper row) and contrast-enhanced T

-weighted post-interventional (lower row) MR

images of exemplary patient cases with spinal metastases and corresponding necrosis zones after RF ablations (arrows).

cesses caused by the coagulation, possible hematoma

as well as a high shape variability. The latter depends

on the former metastasis shape, tissue speciﬁc heat

dissipation, and adjacent anatomical structures (see

Fig. 1).

Currently, there are only few works regarding RF ab-

lation zone segmentation. To our best knowledge,

all published research papers focused on liver tumour

treatment in Computed Tomography (CT) imaging,

none of them dealt with spine RFA necrosis zones

or MR imaging. Therefore, the following state-of-

the-art analysis cannot be or only partially transferred

to ablation zone segmentation in spine MR imag-

ing. Passera et al. (Passera et al., 2013) proposed

a live-wire algorithm and Fuzzy C-Mean clustering

to segment ablation zones in post-interventional RFA

CT scans of the liver. The semi-automatic and user-

guided approach was applied as a 2D slice-wise seg-

mentation and took on average approx. 10 min for

a necrosis zone. Similar to the work by Passera et

al., McCreedy et al. (McCreedy et al., 2006) like-

wise presented a 2D live-wire based approach for CT

imaging, embedded in a RFA registration, segmenta-

tion, and fusion tool. They described the segmenta-

tion step merely superﬁcial and did not present any

quantitative results. Weihusen et al. (Weihusen et al.,

2010) proposed a workﬂow oriented software support

for CT image guided RFA of focal liver malignancies.

As a treatment outcome validation tool, they also in-

cluded a semi-automatic necrosis zone segmentation,

based on a morphological region growing algorithm.

Again, no quantitative results were stated.

Another computer-aided analytic tool was devel-

oped by Bricault et al. (Bricault et al., 2006), which

focused on assessing local recurrences of liver metas-

tases after RFA treatment in CT. For this purpose, a

semi-automatic 3D segmentation approach based on

a watershed algorithm was implemented. On average,

the segmentation took approx. 4 min, but accuracy

results were not stated. A semi-automatic graph-cuts

based approach was proposed by Egger et al. (Egger

et al., 2015) to segment liver tumors in CT imaging.

They achieved accuracies of 77 % Dice on their 12 pa-

tient comprising dataset and had computational times

of only a few seconds. In conclusion, it remains un-

clear, why most of the above mentioned works did not

state any segmentation accuracy results whatsoever,

since the segmentation of the ablation zone is a cru-

cial step towards assessing the efﬁcacy of RFA treat-

ment of metastases and tumours. Furthermore, the

Automatic Segmentation of Necrosis Zones after Radiofrequency Ablation of Spinal Metastases

Figure 2: The U-Net structure used for multi-modal 2D image input, with convolutional layers including batch normalisation,

strided convolutions for downsampling and upsampling layers. The architecture for three-dimensional input is analogous to

the one shown above. A signiﬁcant difference between the two variants is the number of trainable parameters, which is about

2.85 times higher in the 3D case.

previous studies focused only on semi-automatic ap-

proaches for post-interventional CT imaging of liver

tumor ablations. The main objective of this study was

to implement an automatic, deep-learning based seg-

mentation approach for follow-up MR scans of RFAs

of spinal metastases and to assess, which image in-

put w.r.t. dimension and MR sequence for a Convolu-

tional Neural Network (CNN) is most suitable.

2 MATERIALS AND METHOD

2.1 Image Data

We assembled a dataset of 26 follow-up MR images

of patients who underwent radiofrequency ablations

of spinal metastases. These images were acquired be-

tween 1 and 3 days after the intervention including,

among others, sagittal contrast-enhanced T

-weighted

and native T

-weighted MRI sequences. Both se-

quences were chosen, since they are most commonly

used for visual treatment outcome validation by neu-

roradiologists in this particular case. Due to multi-

ple MR scanners, the speciﬁc acquisition parameters,

e.g. magnetic ﬁeld strength or repetition time, varied

within our dataset. The scan resolution within a slice

ranged between the individual cases from 0.45 mm

to 1.25 mm, the spacing between adjacent slices from

3.3 mm to 4.8 mm. The image volumes of each patient

case were pre-processed by a cubic interpolation be-

tween the original number of slices (between 15 and

25) to a ﬁxed number of 64 to yield almost isotropic

spatial resolution and simplify any following process-

ing steps. An experienced neuroradiologists manu-

ally contoured each necrosis slice-wise, thus, the in-

put data could be applied as individual slices or as

patient-wise volumes to our networks.

2.2 Augmentation

Due to our relatively small amount of available data,

we extensively augmented each of the 26 original

MRI volumes using the following techniques:

• Gaussian Blur: The images were burred with a

Gaussian ﬁlter with σ in the range from 0 to 0.5.

• Gamma Transformation: Gamma transformations

with γ in the range from 0.5 to 2 were applied to

modify image intensities.

• Mirroring: Each patient volume was ﬂipped in all

directions. We included vertical ﬂips, i.e. cranio-

caudal, even though it may appear inappropriate,

it had proven to be advantageous for the ﬁnal re-

sults due to the avoidance of fast overﬁtting.

• Scaling: The image volumes were scaled with

randomly chosen factors between 0.6 and 1.4.

• Rotation: Rotations were applied to the im-

age volumes in the range of ±30

◦

around the

transversal axis and between ±20

◦

around the

sagittal axis.

• Elastic Deformations: Random displacement

ﬁelds with subsequently Gaussian smoothing the

grid with a σ ranging between 0 and 0.3 were

used to elastically deform the image volumes (cf.

(Ronneberger et al., 2015)).

• Translation: Finally, each patient volume was

translated in a random cropping manner within a

range of ±20 voxels in sagittal and vertical direc-

tion w.r.t. the center of the necrosis m

and sub-

sequently cropped to patches of the ﬁxed size of

128 × 128 × 64 voxels.

After the augmentation each image volume patch

was whitened by mean subtraction and a subsequent

division by the standard deviation. It was ensured

ICPRAM 2020 - 9th International Conference on Pattern Recognition Applications and Methods

that each patch contained at least fractions of necro-

tizised tissue. Since we used stratiﬁed 6-fold cross-

validation with 26 patient cases, we used a 21/5 (train-

ing/validation) split for 2 folds and 22/4 for the re-

maining four. Each patient volume within the train-

ing set was augmented 1800 times, yielding in to-

tal 37,800 volumetric respectively 2,419,200 cross-

section training samples for both 21/5-split folds

and 39,600 respectively 2,534,400 for the remaining

folds.

2.3 CNN Architecture

We used minimally modiﬁed U-Nets implemented in

Keras and Tensor f low, since the commonly used

U-Net architecture proposed by Ronneberger et al.

(Ronneberger et al., 2015) still represents the state-of-

the-art regarding various medical segmentation tasks

(Isensee et al., 2019) (see Fig. 2). The individual

networks differ in the processing of either 2D or 3D

data and the incorporation of either individual MR se-

quences or multimodal input. Therefore, we applied

2D patches of size 128 × 128 pixels or volumes of

size 128 ×128×64 voxels to our networks with either

one modality channel or two. The convolutional lay-

ers had a kernel size of 3×3 (×3) except the last one,

which applies a 1 × 1 (×1) kernel to reduce the di-

mensionality to the desired output size. A batch nor-

malization followed each convolutional layer. Strided

convolutions (stride of 2) were applied for down-

sampling the image patches. Simpliﬁed upsampling

layers replaced the commonly used up-convolutions,

since it have been found to be equally effective, while

being less computationally expensive (Isensee et al.,

2017). The activation funtion of every convolutional

layer was a Rectiﬁed Linear Unit (ReLU), except the

last one again, where a sigmoid function resulted in

values between 0 and 1 were. The above mentioned

multimodal image input was incorporated in the most

straightforward way, i.e. each MRI sequence was rep-

resented by an input channel. We decided to use a sin-

gle epoch, while the number of iterations was equal to

the number of available samples, since we did not al-

ter any training speciﬁc parameters. Furthermore, we

used Tversky Loss (TL) as proposed by Salehi et al.

(Salehi et al., 2017) as a loss function, which repre-

sents a modiﬁed form of the Tversky index (Tversky,

1977) and is deﬁned as

T L(α, β) =

∑

i=1

)

∑

i=1

) + α

∑

i=1

) + β

∑

i=1

)

(1)

where p

is the probability for a voxel i to be labeled

as necrosis zone and p

as background. For a necro-

sis zone voxel r

is 1 and for a background voxel r

is 0, vice verse for r

. α and β balance the penalities

for false positives and false negatives. Adam (Kingma

and Ba, 2014) was applied as an optimizer with a

learning rate of 0.001. Mini-batch size was 2 sam-

ples for volumetric and 32 for slice-wise input data.

Finally, to produce binary output images a threshold

of 0.5 was applied.

2.4 Experimental Design

We performed multiple experiments with variing net-

work conﬁgurations, i.e. individual or multimodal

image input as well as 2D and 3D images. The ba-

sic network architecture remained unchanged, in or-

der to largely exclude further inﬂuencing factors, e.g.

by varying layer numbers or kernel sizes. Our training

scheme consisted of stratiﬁed 6-fold cross-validation

over disjunct subsets of either ﬁve or four patients

per validation set. The results stated in the following

represent the average of all 6 cross-validation folds.

Since we did not base any training and design de-

cisions on intermediate validation results (no look-

ahead bias) and due to our limited dataset, we have

decided against a separate test set, as it would result

in too few samples for a promising training.

2.5 Evaluation

Expertly annotated necrosis zone segmentations were

produced using co-registered MR sequences of each

patient within a synchronized viewer. The network

described in Section 2.3 was applied to the image

data. To quantify our results, we used Dice simi-

larity coefﬁcients to measure the percentage of vol-

ume overlap, as well as voxel-wise sensitivity (true

positive rate, TPR) and speciﬁcity (true negative rate,

TNR), since some of the related work used both as

quality measurements. The above mentioned are de-

ﬁned as follows:

Dice =

2 |R

∩ P

| + |P

, T PR =

∩ P

, T NR =

∩ P

(2)

with R

and P

as foreground voxels of reference

and prediction and analogously, R

and P

as the cor-

responding background voxels. However, sensitiv-

ity and speciﬁcity are not commonly used to eval-

uate medical image segmentations, since they are

highly sensitive to a segment’s size (Taha and Han-

bury, 2015). The given results were generated exclu-

sively on patient volumes, even if the segmentations

Automatic Segmentation of Necrosis Zones after Radiofrequency Ablation of Spinal Metastases

Table 1: Experimental results for each input conﬁguration depending on the used modalities (contrast − enhanced T

-, T

weighted MRI sequences), as well as a slice-wise (2D) or volume (3D) processing.

2D 3D

ceT

[ceT

+ T

] ceT

[ceT

+ T

]

Dice [%]

mean 76.7 62.2 77.2 72.7 60.4 75.9

median 83.5 65.9 82.3 78.1 63.3 80.7

std 19.0 21.7 15.6 18.7 27.4 17.2

Sensitivity [%]

mean 81.4 69.2 81.6 77.7 63.1 77.8

median 84.5 76.9 86.4 86.2 71.1 82.6

std 17.1 20.3 15.4 21.5 26.5 19.6

Speci f icity [%]

mean 99.2 98.8 99.2 99.1 98.7 99.2

median 99.6 99.1 99.5 99.4 99.0 99.6

std 0.9 1.0 0.8 0.9 1.3 0.9

with 2D input were predicted slice-wise. Thus, the

2D predictions were merged patient-wise.

3 RESULTS AND DISCUSSION

Table 1 shows the results of our experiments. We

achieved Dice scores up to 77.2 ± 15.6 % and sen-

sitivity rates up to 86.4 ± 15.4 %, depending on the

applied MR sequences and input dimension. In this

study a segmentation approach for necrosis zones in

spine MR images was presented. With regards to clin-

ical applicability, the accuracy, automatization, and

computational time are of primary importance. Man-

ual segmentation procedures are time-consuming and

tedious due to their limitation to slice-by-slice pro-

cessing and require up to 10 min per necrosis zone.

That is one of the reasons, such segmentations are not

currently integrated into the clinical routine. There-

fore, an automatic and fast apporach can overcome

the limitations of manual procedures and play a deci-

sive role in improving treatment outcome validation

and ablation protocols. Our proposed method fulﬁlls

these requirements. Since this is (to our best knowl-

edge) the ﬁrst study to tackle necrosis zone segmen-

tation in spine MR images, it is difﬁcult to compare

our results with related work. Furthermore, almost

all relevant works regarding necrosis zone segmenta-

tion did not state any quantitative results, except the

work of Egger et al. (Egger et al., 2015). Our best

results were on par with the results of their semi-

automatic approach (77.2 % vs. 77.0 %), but our

method did not require any user-interaction. Due to

the lack of directly related work, it might be interest-

ing to take works of automatic lesion segmentation

as a similar issue into consideration. Chmelik et al.

(Chmelik et al., 2018) adapted a CNN to vertebral

metastases segmentation in CT images. They stated

a voxel-wise sensitivity rate of 74 % for sclerotic and

71 % for lytic lesions as well as a speciﬁcity rate of

88 % (sclerotic) and 82 % (lytic). Hille et al. (Hille

et al., 2019) applied a CNN to segment spinal metas-

tases in T

- and T

-weighted diagnostic MR images

and achieved Dice scores up to 73.8 %. Although,

our results were superior to those of the mentioned

works, it is worth mentioning, that comparability is

only possible to a very limited extent. Besides the

different imaging method used by Chmelik et al., the

segmentation of spinal metastases represents a rather

more ambitious task due to the high appearance vari-

ability depending on metastatic origin and type (lytic,

sclerotic or mixed). Nonetheless, the segmentation of

necrosis zones in spine MR imaging is hampered by

similar difﬁculties, as there is a variety of anatomical

structures with high image contrasts, similar intensi-

ties, and textures in close proximity. Furthermore, the

necrosis zones are in some cases difﬁcult to distin-

guish from remaining metastases and inﬂammation

processes or possible hematoma could overlap with

the necrosis zone (see Fig. 4, lower row).

With regards to the applied MR sequence, we

achieved the best results with either the contrast-

enhanced T

-weighted data alone or if it was part

of multimodal image input (see Fig. 3). This could

most likely be attributed to the predominantly high

image contrasts between necrosis and surrounding

bone structures, which are additionally enhanced by

the application of contrast agents. Multi-modal in-

put, i.e. combining contrast-enhanced T

-weighted

images with T

-weighted MR data showed only small

improvements regarding the mean accuracy and a

somewhat reduced standard deviation (see Tab. 1).

Applying solely T

-weighted images yielded worse

results, most likely due to the inferior image constrast

ICPRAM 2020 - 9th International Conference on Pattern Recognition Applications and Methods

100

Figure 3: Dice scores and sensitivity rates depending on the imaging modality and input dimension. Box edges mark the 25th

and 75th percentiles, the central box line marks the median value.

Figure 4: Three exemplary patient cases, representing very high (upper row), average (middle row) and unsatisfactory (lower

row) segmentation accuracies produced with 3D image input. Corresponding Dice scores are stated in the lower right corners.

Green contours display the expertly annotated data as ground truth and blue contours represent our automatically produced

segmentations. From left to right: (a) original T

-weighted MRI sequence, (b) original T

-weighted MRI sequence, (c) result

with only T

-weighted image data, (d) result with only T

-weighted image data, (e) result with combined T

- and T

-weighted

image data.

between necrosis and surrounding tissues. Hence,

they rather support and improve robustness in combi-

nation with the contrast-enhanced T

-weighted input.

With respect to the input dimension, it was found that

on average 2D image input yielded higher scores than

volumetric image input, which could be attributed to

the higher number of trainable parameters (1,400,000

vs. 4,000,000) to be optimized and therefore, in-

creased network complexity.

Automatic Segmentation of Necrosis Zones after Radiofrequency Ablation of Spinal Metastases

101

4 CONCLUSION

Automatic necrosis zone segmentation in follow-up

MR scans after RF ablations of spinal metastases has

the potential to quantify and objectify the treatment

outcome validation. It provides important informa-

tion regarding the improvement of ablation proce-

dures and it may help understanding and predicting

possible tumor reoccurrence. We proposed a CNN-

based segmentation approach and examined the im-

pact of various input modalities and dimensions on

the segmentation accuracy. Our results were on par

with those of Egger et al. (Egger et al., 2015), which

were the only quantitative results available (77.2.0 %

vs 77 %), altough the latter focused on necrotized

liver lesions in CT imaging. Overall, our study in-

dicates promising results and constitutes a valuale ap-

proach towards this ambitious and challenging issue.

ACKNOWLEDGEMENTS

This work was supported by the German Ministry

of Education and Research (13GW0095A) within the

STIMULATE research campus.

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