BADRESC: Brain Anomaly Detection based on Registration Errors and

Supervoxel Classiﬁcation

Samuel B. Martins

1,2,3 a

, Alexandre X. Falc

1 b

and Alexandru C. Telea

4 c

Laboratory of Image Data Science (LIDS), Institute of Computing, University of Campinas, Brazil

Bernoulli Institute, University of Groningen, The Netherlands

Federal Institute of S

ao Paulo, Campinas, Brazil

Department of Information and Computing Sciences, Utrecht University, The Netherlands

Keywords:

Brain Anomaly Detection, Supervoxel Segmentation, One-class Classiﬁcation, Registration Errors, MRI.

Abstract:

Automatic detection of brain anomalies in MR images is very challenging and complex due to intensity sim-

ilarity between lesions and normal tissues as well as the large variability in shape, size, and location among

different anomalies. Inspired by groupwise shape analysis, we adapt a recent fully unsupervised supervoxel-

based approach (SAAD) — designed for abnormal asymmetry detection of the hemispheres — to detect brain

anomalies from registration errors. Our method, called BADRESC, extracts supervoxels inside the right and

left hemispheres, cerebellum, and brainstem, models registration errors for each supervoxel, and treats outliers

as anomalies. Experimental results on MR-T1 brain images of stroke patients show that BADRESC attains

similar detection rate for hemispheric lesions in comparison to SAAD with substantially less false positives.

It also presents promising detection scores for lesions in the cerebellum and brainstem.

1 INTRODUCTION

The visual slice-by-slice inspection of abnormal tis-

sues in magnetic resonance (MR) 3D brain images by

a clinician is the most commonly procedure for early

diagnosis and follow-up of brain disorders. This pro-

cess is very laborious, time-consuming, easily prone

to errors, and impracticable to be performed at a large

scale. Several automatic methods address these difﬁ-

culties by delineating brain anomalies as accurate as

clinicians. However, this goal is very challenging and

complex due to the large variability in shape, size, and

location among different anomalies (see e.g., Fig. 1).

Most automatic brain lesion detec-

tion/segmentation methods train a discriminative

model from training images — which must be previ-

ously annotated (e.g., lesion segmentation masks) by

specialists — to delineate anomalies by classifying

voxels or regions of the target image (Goetz and

et al., 2014; Pinto et al., 2015; Soltaninejad and

et al., 2017). Traditional image features (e.g., edge

detectors and texture features) and deep feature

https://orcid.org/0000-0002-2894-3911

https://orcid.org/0000-0002-2914-5380

https://orcid.org/0000-0003-0750-0502

Figure 1: Axial slices of three stroke patients from the AT-

LAS dataset (Liew and et al., 2018) with lesions (ground-

truth borders in red) that signiﬁcantly differ in location,

shape, and size.

representations (e.g., convolutional features) are

commonly used (Goetz and et al., 2014; Soltaninejad

and et al., 2017; Kooi et al., 2017; Aslani et al.,

2018). Some works propose a groupwise shape

analysis based on estimating the deformation ﬁeld

between a target image and a template (reference

image) after image registration (Gao et al., 2014;

Shakeri and et al., 2016).

However, these methods commonly have three

main limitations. First, they require a large num-

ber of high-quality annotated training images, which

is not easily found for most medical image analysis

problems (Akkus and et al., 2017; Thyreau and et al.,

2018). Second, they are only designed for the lesions

found in the training set. Third, some methods still

Martins, S., Falcão, A. and Telea, A.

BADRESC: Brain Anomaly Detection based on Registration Errors and Supervoxel Classiﬁcation.

DOI: 10.5220/0008987800740081

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 2: BIOIMAGING, pages 74-81

ISBN: 978-989-758-398-8; ISSN: 2184-4305

require weight ﬁne-tuning (retraining) when used for

a new set of images due to image variability across

scanners and acquisition protocols, limiting its appli-

cation to clinical routine.

Unsupervised anomaly detection approaches aim

to circumvent the above limitations by encoding gen-

eral knowledge or assumptions (priors) from healthy

tissues from control images of healthy subjects only.

Any outlier who breaks such general priors is then

considered as an anomaly (Guo et al., 2015).

Since many neurological diseases are asso-

ciated with abnormal brain asymmetries (Wang

and et al., 2001), an unsupervised method called

Supervoxel-based Abnormal Asymmetry Detection

(SAAD) (Martins et al., 2019b) was recently pro-

posed to detect abnormal asymmetries in MR brain

images. SAAD presents a mechanism for asymmetry

detection that consists of three steps: (i) it registers

all images to the same symmetric template and then

computes asymmetries between the two hemispheres

by using their mid-sagittal plane (MSP) as reference;

(ii) a supervoxel segmentation method, named Sym-

mISF, is used to extract pairs of symmetric supervox-

els from the left and right hemispheres for each test

image, guided by their asymmetries. Supervoxels de-

ﬁne more meaningful volumes of interest for analysis

than regular 3D patches; and (iii) each pair generates

a local one-class classiﬁer trained on control images

to ﬁnd supervoxels with abnormal asymmetries on the

test image. SAAD was further extended to detect ab-

normal asymmetries in the own native image space of

each test image (Martins et al., 2019c).

Although SAAD claims to obtain higher detec-

tion accuracy even for very small lesions compared

to state-of-the-art detection methods, its analysis is

limited to asymmetric anomalies in the brain hemi-

spheres, ignoring lesions in the cerebellum and brain-

stem. Moreover, if the same lesion is localized in both

hemispheres roughly in the same position (e.g., some

cases of multiple sclerosis), it is not detected due to

the lack of asymmetries.

Inspired by groupwise shape analysis, in this work

we present BADRESC, an unsupervised method for

Brain Anomaly Detection based on Registration Er-

rors and Supervoxel Classiﬁcation in 3T MR-T1 im-

ages of the brain. After registering a target image to

a common template with only healthy tissues by de-

formable registration, BADRESC assumes that regis-

tration errors for anomalies are considerably different

of the registration errors for healthy tissues. Thus,

BADRESC adapts the SAAD framework as follows.

First, it replaces the asymmetry maps with registra-

tion errors. A robust preprocessing is considered to

improve the quality of image registration. Second, it

expands the anomaly analysis to four objects of in-

terest — right and left hemispheres, cerebellum, and

brainstem — by extracting supervoxels for each one

separately. Finally, each supervoxel generates a local

one-class classiﬁer for healthy tissues to detect out-

liers as anomalies.

We compare BADRESC with SAAD for the de-

tection of hemispheric lesions on 3D MR-T1 brain

images of stroke patients. Experimental results shows

that BADRESC attains similar detection rates to

SAAD with considerably less false positives. Addi-

tionally, BADRESC presents promising results for the

detection of lesions in the cerebellum and brainstem.

2 DESCRIPTION OF BADRESC

We next describe the BADRESC method (see also

Fig. 2). The method consists of ﬁve steps: 3D im-

age preprocessing, image registration, registration er-

ror computation, supervoxel segmentation, and clas-

siﬁcation, described next. The brain regions/objects

of interest in this work are the right hemisphere, left

hemisphere, cerebellum, and brainstem.

2.1 3D Image Preprocessing and

Registration

MR images are altered by image acquisition prob-

lems such as noise and intensity heterogeneity. This

makes the automated analysis very challenging since

the intensities of the same tissues vary across the

image (Pereira et al., 2016). To alleviate these and

make images more similar to each other, we use typi-

cal preprocessing steps known in the literature (Juan-

Albarrac

ın et al., 2015; Pereira et al., 2016; Manj

and Coup

e, 2016; Martins et al., 2019b), as shown in

Fig. 3.

Initially, we perform the same preprocessing steps

of SAAD by applying bias ﬁeld correction with

N4 (Tustison et al., 2010), followed by median ﬁl-

tering for noise reduction, and linear intensity nor-

malization within [0, 4095]. Since voxels from irrel-

evant tissues/organs for the addressed problem (e.g.,

neck and bones) can negatively impact the image reg-

istration and intensity normalization, we use the prob-

abilistic atlas-based method AdaPro (Martins et al.,

2019a) to segment the regions of interest (see Fig. 3a-

c).

To attenuate differences in brightness and con-

trast among images, we apply a histogram match-

ing between the segmented images and the template.

This operation only considers the voxels inside the re-

gions of interest (see Fig. 3d). We then perform de-

BADRESC: Brain Anomaly Detection based on Registration Errors and Supervoxel Classiﬁcation

steps 1, 2, 3, 5, 6, 7, 8

Template

Train. Control

Images

Registration

Pre-

Processing

Compute

Reg. Errors

Average

Reg. Errors

Registration

Pre-

Processing

1 2 3

5 6

Compute

Reg. Errors

Supervoxel

Segmentation

Feature

Extraction

Classification

X AR

LI AR

Test Image

Datasets

Supervoxel 1

Supervoxel N

Supervoxel 2

Figure 2: Pipeline of BADRESC. The upper blue part is computed previously (ofﬂine). The bottom orange part is computed

for each test image. The template (reference image) is used in both parts (Steps 1, 2, 3, 5, 6, 7, and 8). Figure based on (Martins

et al., 2019b; Martins et al., 2019c).

(a) (b) (c)

(e)(d)

Figure 3: Preprocessing and registration steps. (a) Axial

slice of the brain template (reference image). (b) Axial slice

of a raw test image. The dashed line shows its mid-sagittal

plane (MSP) and the arrow indicates a stroke lesion. (c)

Test image after bias ﬁeld correction, noise ﬁltering, MSP

alignment, and brain segmentation. (d) Histogram matching

between (c) and the template. (e) Final preprocessed image

after deformable registration and histogram matching with

the template.

formable registration to place all images in the coor-

dinate space of the ICBM 2009c Nonlinear Symmet-

ric template (Fonov and et al., 2009). Since the im-

age registration technique has a critical impact on the

analysis, we use Elastix (Klein et al., 2010), a popu-

lar and accurate image registration method.

Finally,

we perform another histogram matching between the

registered images and the template (see Fig. 3e).

We used the par0000 ﬁles available at http://elastix.

bigr.nl/wiki/index.php

2.2 Registration Error Computation

When registering images to a common template with

only healthy tissues, we expect that registration errors

(REs) — voxel-wise absolute differences between the

registered image and the template — are lower and

present a different pattern compared to anomalies (see

Fig. 4e). However, some healthy structures in the

cortex, such as gyri and sulci, present high REs due

to their complex shapes and immense variability be-

tween subjects — observe the cortex of the template

and the registered image in Figs. 4a and 4d; note its

resulting REs in Fig. 4e. To avoid detecting false pos-

itives in this region, some attenuation process is re-

quired.

Let T be the template (Fig. 4a) and M

its pre-

deﬁned brain segmentation mask for the right hemi-

sphere, left hemisphere, cerebellum, and brainstem

(background voxels have label 0 and each object has

a different label). Let X = {X

, ··· , X

} be the set of k

registered training images (output of Step 2 in Fig. 2)

and I the test image after preprocessing and registra-

tion (output of Step 6 in Fig. 2; see also Fig. 4d).

Firstly, we compute the euclidean distance trans-

form (EDT) for each object of M

and normalize the

distances within [0, 1] to build the map E (Fig. 4b).

Next, we obtain the set of registration errors R

for all

X by computing the voxel-wise absolute differences

between X and T (Step 3 in Fig. 2; see also Fig. 4e).

For each training image X

∈ X, we attenuate REs in

its cortex such that for each voxel v ∈ X

f (v) = 1 − (E(v) − 1)

(v) = R

(v) · f (v)

(1)

BIOIMAGING 2020 - 7th International Conference on Bioimaging

(a) (b) (c) (d) (e) (f) (g)

1.0

0.0

Figure 4: Registration error computation. (a) Axial slice of the brain template. (b) Euclidean Distance Transform (EDT)

normalized within [0, 1] computed for the brain segmentation mask deﬁned for the template. Brain borders are shown only

for illustration purposes. (c) Common registration errors for control images. (d) Axial slice of a test stroke image after

preprocessing and registration in (a). The arrow indicates the stroke lesion. (e) Registration errors. (f) Attenuation of (e) for

the cortex based on the EDT. (g) Final registration errors for the test image: positive values of the subtraction between (f) and

(c).

where E(v) is the euclidean distance for the voxel v,

f (v) is its attenuation factor within [0, 1], and AR

the map with the attenuated REs for X

. Thus, REs of

voxels close to the brain borders are extremely atten-

uated whereas those from voxels far from the borders

are slightly impacted (see Fig. 4f). A downside of this

approach is that subtle lesions in the cortex tend to be

missed.

In order to even ignore REs caused by noises or

small intensity differences in regions/tissues far from

the cortex, we create a common registration error map

by averaging the attenuated REs from AR

(out-

put of Step 4 in Fig. 2; see also Fig. 4c). Finally,

we repeat the same steps to compute the attenuated

REs for the test image I and then subtract AR

from

them. Resulting positive values form a ﬁnal attenu-

ated registration error map AR

for I (output of Step 7

in Fig. 2; see also Fig. 4g).

2.3 Supervoxel Segmentation

The direct comparison between the registered image

and its template, or even between large 3D regular

patches, is not useful as it will not tell us where

small-scale REs occur — a similar parallel is done for

asymmetries in (Martins et al., 2019c). Conversely,

a voxel-wise comparison is risky, since individual

voxels contain too little information to capture REs.

These difﬁculties motivate the use of supervoxels as

the unit of comparison (Step 8 in Fig. 2).

Inspired by the SymmISF method (Martins et al.,

2019b) used in SAAD for symmetrical supervoxel

segmentation, we propose a new technique that ex-

tracts supervoxels in the brain guided by registration

errors, as shown in Fig. 5. Our supervoxel segmen-

tation is also based on the recent Iterative Spanning

Forest framework (Vargas-Mu

noz et al., 2019) for su-

perpixel segmentation and has three steps: (i) seed es-

timation; (ii) connected supervoxel delineation (mul-

tiple iterations); and (iii) seed recomputation to im-

prove delineation, as follows.

Recall a template T , a preprocessed and registered

test image I, and its attenuated registration error map

. We ﬁnd the initial seeds by selecting one seed

per local maximum in AR

(see the seeds in Fig. 5).

We compute the local maxima of the foreground of

a binarized AR

at γ × τ, where τ is Otsu’s thresh-

old (Otsu, 1979). The higher the factor γ is, the lower

is the number of components in the binarized AR

. We

extend the seed-set with a ﬁxed number (100) of seeds

by uniform grid sampling the regions with low REs of

the binarized image.

By stacking I and T as the input 2-band volume

(see Fig. 5), we apply ISF inside each object of inter-

est separately from the initial seeds. ISF relies on a

cost function controlled by two parameters: α and β.

The results are label maps in which each supervoxel

is assigned to a distinct number/color. All labels are

then combined and relabeled to build the ﬁnal super-

voxel map L (output of Step 8 in Fig. 2).

2.4 Feature Extraction and

Classiﬁcation

The feature extraction and classiﬁcation steps are very

similar to those of SAAD (Martins et al., 2019b). For

each test image I, each supervoxels in L is used to cre-

ate a one-class classiﬁer using as feature vector the

normalized histogram of the attenuated REs in AR

(Step 9 in Fig. 2). This implicitly considers the po-

sition of the supervoxels in the brain during classiﬁ-

cation. BADRESC uses the one-class linear Support

Vector Machine (oc-SVM) for this task (Manevitz

and Yousef, 2001). The classiﬁers are trained from

healthy control images only and used to identify out-

lier supervoxels with abnormal REs in I (Step 10 in

Fig. 2).

BADRESC: Brain Anomaly Detection based on Registration Errors and Supervoxel Classiﬁcation

seed

estimation

ISF

concatenate

volumes

template

(reference image)

registration errors

seeds

supervoxels

preprocessed

test volume

(3D image)

2 -band

stacked volume

Figure 5: Pipeline of the proposed supervoxel segmentation. The method stacks the input preprocessed test 3D image (seg-

mented objects are colored) with the template to build a 2-band volume. An initial seed set is obtained from the registration

errors of the test image. For each object of the segmentation brain mask, the ISF framework (Vargas-Mu

noz et al., 2019)

estimates supervoxels inside the object from the initial seeds. Resulting supervoxels are combined and relabeled to form the

ﬁnal label map.

3 EXPERIMENTS

To evaluate the proposed method, we need datasets

with volumetric MR-T1 brain images (i) from healthy

subjects for training, and (ii) with lesions of different

appearance (especially small ones) containing their

segmentation masks. First, we considered the Cam-

Can dataset (Taylor et al., 2017) which has 653 MR-

T1 images of 3T from healthy men and women be-

tween 18 and 88 years. As far as we know, CamCan

is the largest public dataset with 3D images of healthy

subjects acquired from different scanners. In order

to avoid noisy data in the training set, we removed

some images with artifacts or bad acquisition after a

visual inspection in all MR-T1 images, yielding 524

images.

For testing, we chose the Anatomical Tracings of

Lesions After Stroke (ATLAS) public dataset release

1.2 (Liew and et al., 2018) in our experiments. AT-

LAS is a very challenging dataset with a large vari-

ety of manually annotated lesions and images of 1.5T

and 3T acquired from different scanners. It contains

heterogeneous lesions that differ in size, shape, and

location (see Fig. 1). All images only have a mask

with a stroke region ignoring other possible anoma-

lies caused by those lesions. Current state-of-the-art

segmentation results for ATLAS from a supervised

method based on U-Net are inaccurate yet (Qi and

et al., 2019).

A link with the considered images can be found

at https://github.com/lidsunicamp/BIOIMAGING20

BADRESC

Since the considered training images have a 3T

ﬁeld strength, we selected all 3T images from AT-

LAS for analysis (total of 269 images). All im-

ages were registered into the coordinate space of

ICBM 2009c Nonlinear Symmetric template (Fonov

and et al., 2009) and preprocessed as outlined in Sec-

tion 2.1.

We compared BADRESC against the SAAD

method proposed in (Martins et al., 2019b), which in

turn was also evaluated with the ATLAS dataset as

reported in (Martins et al., 2019b). For a fair compar-

ison, we compared both methods for all 3T images

which only contain lesions in the hemispheres. Addi-

tionally, we evaluated BADRESC for the 3T images

with stroke lesions in the cerebellum and brainstem.

We used the following parameters for BADRESC,

empirically obtained from the observation on a few

training control images: α = 0.06, β = 5.0, γ = 3,

histogram of 128 bins, and ν = 0.01 for the linear oc-

SVM.

We proposed a set of metrics to evaluate the de-

tection quality, as follows. We start computing the

detection rate based on at least 15% overlap between

supervoxels detected by the methods and lesions la-

beled in ATLAS (Table 1, row 1). We then provided

false positive (FP) scores in terms of both voxels and

supervoxels with respect to the ground-truth stroke le-

sions of ATLAS. We ﬁrst computed the mean rate of

FP voxels, i.e., incorrectly classiﬁed as abnormal (Ta-

ble 1, row 2), with respect to all classiﬁed voxels from

the analyzed object(s) — i.e., the total number of vox-

els inside the right hemisphere for SAAD and all vox-

BIOIMAGING 2020 - 7th International Conference on Bioimaging

Table 1: Quantitative results for images from the ATLAS dataset with stroke lesions in the hemispheres, cerebellum, and

brainstem. Higher detection rate means better accuracy. Lower false positive rate means better accuracy.

Hemispheres

Cerebellum and

Brainstem

SAAD BADRESC BADRESC

Detection Rate

0.8324 0.8298 0.6829

False Positive

Voxel Rate

0.050 ± 0.027 0.005 ± 0.006 0.005 ± 0.004

Number of False

Positive Supervoxels

53.84 ± 19.64 21.48 ± 13.82 25.63 ± 15.64

False Positive

Supervoxel Rate

0.14 ± 0.05 0.104 ± 0.069 0.097 ± 0.049

SAAD

image 1 image 2 image 3 image 4 image 5 image 6

BADRESC

no analysis of the

cerebellum and brainstem

Figure 6: Results on ATLAS dataset. Each column is a test image: Images 1-3 have lesions in the hemispheres, Image

4 contains a lesion in the brainstem whereas Images 5-6 have lesions in the cerebellum. First row: Ground-truth lesion

segmentations. Second row: Results of BADRESC. Third row: Results of SAAD. Arrows indicate undetected lesions. Since

SAAD only detects lesions in the hemispheres, there are no results for Images 4-6.

els from the hemispheres, cerebellum, and brainstem

for BADRESC.

At the next level, we estimated FP supervoxels

as those whose voxels overlap less than 15% with

ground-truth lesion voxels. We computed the mean

number of FP supervoxels and its proportion with re-

spect to the total number of supervoxels from the an-

alyzed object(s) (Table 1, rows 3 and 4). The ﬁrst

metric gives us an estimation of the visual-inspection

user effort. The second metric checks how imprecise

is the detection regarding the total number of regions

that the user has to visually analyze.

4 RESULTS AND DISCUSSION

Table 1 summarizes all quantitative results whereas

Fig. 6 presents some visual results. Although SAAD

presents a slightly better detection rate for hemi-

spheric lesions (0.8324) compared to BADRESC

(0.8298), it consistently presents worse FP rates (see

its FP in Fig. 6). SAAD incorrectly classiﬁes 14% of

supervoxels on average — which consists of 5% of

the analyzed voxels in the hemisphere. Conversely,

as Table 1 shows, BADRESC presents considerably

less FP supervoxels than SAAD (average of 21.48 su-

pervoxels against 53.84). This corresponds to 10.4%

of analyzed supervoxels and less than 1% of voxels in

the entire brain (see Fig. 6).

SAAD is not able to detect lesions with low asym-

metries, even if they are well-contrasted with their

surrounding tissues. BADRESC does not have this

limitation (compare the results for Image 2 in Fig. 6).

However, both methods are not robust to detect very

small-scale anomalies (Fig. 6, Image 3).

BADRESC is less accurate to detect lesions in the

BADRESC: Brain Anomaly Detection based on Registration Errors and Supervoxel Classiﬁcation

cerebellum and brainstem (detection rate of 0.6829).

Indeed, some lesions seems to be more challenging,

specially in the cerebellum, whose appearances are

similar to their surrounding tissues (Fig. 6, Image

6). However, its FP scores are similar to those of

hemispheric lesions which conﬁrms the stability of

the method (compare rows 2-4 for BADRESC in Ta-

ble 1).

5 CONCLUSIONS

We presented a new unsupervised method for brain

anomaly detection that combines registration errors

and supervoxel classiﬁcation. Our approach, named

BADRESC, adapts a recent supervoxel-based ap-

proach (SAAD) to detect outliers as anomalies from

registration errors in the hemispheres, cerebellum,

and brainstem. BADRESC was validated on 3T MR-

T1 images of stroke patients with annotated lesions,

attaining similar detection accuracy to SAAD for le-

sions in the hemispheres and substantially less false

positives. BADRESC also detects lesions in the cere-

bellum and brainstem with promising results.

For future work, we intend to improve BADRESC

by optimizing its parameters and using additional vi-

sual analytics techniques to improve seeding and fur-

ther investigate other anomaly features and classiﬁers

to yield better detection rates, specially for the cere-

bellum and brainstem.

ACKNOWLEDGEMENTS

The authors thank CNPq (303808/2018-7), and

FAPESP (2014/12236-1) for the ﬁnancial support.

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