=Paper= {{Paper |id=Vol-2380/paper_140 |storemode=property |title=Lung Graph-Model Classification with SVM and CNN for Tuberculosis Severity Assessment and Automatic CT Report Generation |pdfUrl=https://ceur-ws.org/Vol-2380/paper_140.pdf |volume=Vol-2380 |authors=Yashin Dicente Cid,Henning Müller |dblpUrl=https://dblp.org/rec/conf/clef/CidM19 }} ==Lung Graph-Model Classification with SVM and CNN for Tuberculosis Severity Assessment and Automatic CT Report Generation== https://ceur-ws.org/Vol-2380/paper_140.pdf

Lung Graph–Model Classification with SVM and
CNN for Tuberculosis Severity Assessment and
Automatic CT Report Generation
Participation in the ImageCLEF 2019 Tuberculosis Task

Yashin Dicente Cid1 and Henning Müller1,2
1
University of Applied Sciences Western Switzerland (HES–SO), Sierre, Switzerland;
2
University of Geneva, Switzerland
yashin.dicente@hevs.ch

Abstract. In 2019, ImageCLEF proposed a task using CT (Computed
Tomography) scans of patients with tuberculosis (TB). The task was
divided into two subtasks: TB severity assessment (SVR subtask) and
automatic CT report generation (CTR subtask). In this work we present
our participation in the task. We participated with a graph model of the
lungs with a morphology–based structure that was previously validated
for the detection of TB types. The graph is based on a parcellation of
the lung fields into supervoxels, where each region is identified as a node
of the graph. A weighted edge is defined between nodes representing
adjacent regions. The associated weight is computed as the distance be-
tween 3D texture descriptors extracted from the two connected regions.
This model encodes the texture distribution along the lungs, making it
suitable for detecting the tissue abnormalities present in TB patients.
In this work we explore two techniques to classify these graphs: (i) a
lung descriptor vector based on the aggregation of graph centrality mea-
sures and (ii) a set of 2D histograms encoding the binary distribution of
node features. The final classification is performed with support vector
machines for the lung descriptor vector and with convolutional neural
networks for the 2D histograms. The results show the strength of the
technique, leading to 6th and 3rd place in the SVR and CTR subtasks,
respectively.

Keywords: Lung Graph Model, Graph Kernel, CNN, 3D Texture Anal-
ysis, Computed Tomography, Tuberculosis,

1 Introduction
ImageCLEF (the image retrieval and analysis evaluation campaign of the Cross–
Language Evaluation Forum, CLEF) has organized challenges on image classi-
fication and retrieval since 2003 [1]. Since 2004, a medical image analysis and
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). CLEF 2019, 9-12 Septem-
ber 2019, Lugano, Switzerland.
retrieval task (ImageCLEF) has been organized [2, 3], usually based on tasks
specifically requested by radiologists [4] or making knowledge of public visual
data [5]. The ImageCLEF 2019 [6] challenge included for the third year a task
based on CT (Computed Tomography) volumes of patients with tuberculosis
(TB), the ImageCLEF 2019 TB task [7]. In this task, a dataset of lung CT
scans with associated meta–data was provided and two subtasks were proposed.
The 2018 edition of the ImageCLEF TB task [8] already included the TB sever-
ity score prediction (SVR) subtask and this year it included the automatic CT
report generation (CTR) subtask.
When tuberculosis affects the lungs, several visual patterns can be seen in a
CT image, characteristic of the underlaying TB type. Moreover, their spread into
parts of the lung is a good indicator of the severity of the diseases. However, the
final diagnosis usually requires other analyses rather than only the images [9]. We
participated in the 2017 and 2018 challenge with a texture–based graph model
of the lungs [10, 11] capable to offer a global descriptor of the lung texture.
In both cases, the structure underlaying the graph was based on a geometric
parcellation of the lung fields into a fixed number of regions. This graph model
offers the possibility of describing each patient with a vector of weights that has
the same size for all patients, hence enabling its direct comparison. Later on, we
developed a new graph model with morphology–based structure that required a
graph kernel transformation in order to compare between patients. This model
was tested on the same data from the ImageCLEF 2018 TB task, obtaining
much higher results than the previous graph with fixed structure in the TB
type classification task [12]. For the 2019 edition of the TB task we used our
latest graph model with morphology–based structure and we investigated two
approaches to classify the resulting data: the first is based on the graph kernel
developed in [12] and the second one, proposed by Tixier et al. [13], is based
on transforming the graphs in a set of 2D images and apply a 2D convolutional
neural network (CNN).
The following section contains a brief overview of the subtasks and datasets
of the ImageCLEF 2019 TB task. More detailed information on the subtasks
and data can be found in the overview article of the task [7]. Section 3 first sum-
marizes the process to build the texture–based graph model of the lungs with
morphology–based structure, followed by the graph kernel and the 2D CNN ap-
proaches. The results obtained by these approaches in the two subtasks are shown
in Section 4. Finally, Section 5 concludes our participation in this challenge.

2 Subtasks and Dataset

The ImageCLEF 2019 TB task proposed two subtasks: i) Severity score assess-
ment (SVR subtask) and ii) automatic CT report generation (CTR subtask).
Both subtasks share the same dataset, composed of 335 volumetric chest CT
scans with associated meta–data. Moreover, automatic segmentations of the
lungs were provided by the organizers, obtained with the method described
in [14]. The masks provided were used and no other lung segmentation was
attempted in this work.
The challenge was divided into two phases. In the first phase, the organizers
released a set of 218 CT volumes for training, along with their lung masks,
ground truth labels and meta–data. In the second phase, the test set (117 CT
scans) with the corresponding lung segmentations and meta–data was provided.
The ground truth for the test data was never made available. In our approach
we only used the CT images and the lung masks but not the meta–data. The
evaluation on the test set was performed by the organizers after the scheduled
deadline. The exact details of the dataset, such as number of patients per class,
are included in the overview article of the TB task [7].
The SVR subtask consisted of assigning a TB severity score to each CT
image. The original score varied between 1 and 5 and the organizers reduced it to
two scores, HIGH and LOW, therefore transforming the original 5–class problem
into a binary classification. In the case of the CTR subtask, the objective was
to detect the presence/absence of 6 TB related CT–findings in each CT scan.
In our approach we treated this subtask as a multi–binary problem, assuming
complete independence between the findings. For the 7 binary labels to detect
(TB severity + 6 CT–findings) we used the same graph model, only varying the
training labels in the last classification step of our pipeline.

3 Methods

3.1 Lung Graph Model

To build the graph model of the lungs with morphology–based structure we
followed the pipeline introduced in [12] and that is shown in Figure 1. The key
details of each of the steps in the pipeline, including the preprocessing applied
to the CT scans, follow. Unless otherwise stated, any other detail remains the
same as in the original work [12].

Lung segmentation Supervoxelization G = (N , E, w)
AAACFHicbVDLSgMxFM3UV62vUZdugkWoKGWmCroRiiK6kgr2Ae1QMmmmDc1khiSjlGE+wo2/4saFIm5duPNvzLSlaOuBwDnn3kvuPW7IqFSW9W1k5uYXFpeyy7mV1bX1DXNzqyaDSGBSxQELRMNFkjDKSVVRxUgjFAT5LiN1t3+R1uv3REga8Ds1CInjoy6nHsVIaattHrR8pHoYsfgqgWewMJE3ySGciEstHvbbZt4qWkPAWWKPSR6MUWmbX61OgCOfcIUZkrJpW6FyYiQUxYwkuVYkSYhwH3VJU1OOfCKdeHhUAve004FeIPTjCg7d3xMx8qUc+K7uTNeU07XU/K/WjJR36sSUh5EiHI8+8iIGVQDThGCHCoIVG2iCsKB6V4h7SCCsdI45HYI9ffIsqZWK9lGxdHucL5+P48iCHbALCsAGJ6AMrkEFVAEGj+AZvII348l4Md6Nj1FrxhjPbIM/MD5/ACjUnZ4=

Fig. 1. Construction pipeline of the graph model consisting of 3 steps: lung parcellation,
regional feature extraction and graph entity formation.
Preprocessing: For our approach we use rotation–invariant 3D texture features
that require having isometric voxels. We first made the 3D images and the lung
masks isometric. After analyzing the different resolutions present in the dataset,
a voxel size of 1 mm was selected to capture the maximum textural information.

Supervoxelization: The parcellation of the lung fields was done using a 3D
supervoxelization algorithm based on a generalization for 3D volumes of the SLIC
algorithm [15, 16], that created homogeneous regions in terms of Hounsfield units
(HU).

Regional 3D Texture Features: Each region in the supervoxelization was
described using two state–of–the–art 3D texture descriptors: the histogram of
oriented gradients based on the Fourier transform HOG (FHOG) introduced
in [17] and the locally–oriented 3D Riesz–wavelet transform introduced in [18].
The former resulted in a 28–dimensional vector (fH (v) ∈ R28 ) and the latter in
a 40–dimensional vector (fR (v) ∈ R40 ) for each voxel v in the region. Finally,
given a region r, we extracted the mean (µ) and standard deviation (σ) of the
above mentioned features inside the region, i.e. µ(fH (r)), σ(fH (r)), µ(fR (r)) and
σ(fR (r)). Hence, four feature vectors were obtained for each region.

Texture–based Graph Model of the Lungs: We used a weighted undirected
graph G(N , E, w) using the supervoxelization as underlying structure, i.e each
node Ni ∈ N corresponds to a region ri in the supervoxelization. Then, an
undirected edge Ei,j with associated weight wi,j is defined between nodes Ni
and Nj if regions ri and rj are 3D adjacent inside the lung parcellation. The
weight wi,j is defined as the correlation distance between the regional feature
vectors.
Since we used four regional feature vectors (µH , σ H , µR and σ R ), four
graphs with same edges but different edge–weights were generated for each pa-
tient: GµH , GσH , GµR and GσR .

3.2 Graph Classification

At this point, each patient is described with four graphs that share the same
number of nodes and edges, but differ from any graph from another patient.
Therefore, a method is required that translates these graphs into a common
space where they can be compared. In this section we describe the two methods
tested in 2019. One is based on extracting node centrality measures and the
second one on describing each graph as a set of 2D images.

3.3 Graph Classification using Graph Kernels

In this case, we described each graph with a fixed number of values derived
from graph measures. This method was developed in [12] and it is summarized
here: For each node N in a graph G we computed five graph centrality measures:
w
the weighted degree dw (N ), the relative weighted degree dr (N ) = dd(N
(N )
) , the
w
weighted closeness cw (N ), the relative weighted closeness cr (N ) = cd(N (N )
) and
w
the weighted betweenness b (N ). Considering the entire set of nodes N in G,
each of these five measures was modeled as a distribution X: DGw = {dw (N )},
DGr = {dr (N )}, CGw = {cw (N )}, CGr = {cr (N )} and BGw = {bw (N )}, where
N ∈ N . Then, we described each of these distributions using 10 equidistant
percentiles from 0 to 100, with percentiles 0 (π1 (X)) and 100 (π10 (X)) being the
minimum and maximum values in the distribution, respectively. Let π(X) =
(π1 (X), . . . , π10 (X)) be the vector composed of the 10 percentiles πk (X) of a
distribution X. Then, our graph–based lung descriptor is defined as:

ω(G) = (µw , π(DGw ), π(DGr ), π(CGw ), π(CGr ), π(BGw ))

where µw is the mean of the weights in the graph.
For each patient p with graph model Gp , its graph–based lung descriptor
ω(Gp ) belongs to R51 . From now on, ω(Gp ) is referred to as ω f ,p , where f cor-
responds to the regional feature used to build the graph Gp (see Section 3.1).
Since four regional features were computed in each region of the lung parcel-
lation (µH (r), σ H (r), µR (r), and σ R (r)), the final lung descriptor ω̂ p used in
our experiments was defined as the concatenation of the four graph–based lung
descriptors:
ω̂ p = (ω µH ,p , ω σH ,p , ω µR ,p , ω σR ,p ) ∈ R204 .
Finally, these lung descriptor vectors ω̂ p were fed into a Support Vector Ma-
chine (SVM) classifier to provide the prediction scores. For each of the 7 binary
problems (see Section 2), feature normalization and dimensionality reduction was
applied with respect to the training data before the SVM classifier was used. The
SVM parameters were found by grid–search and 10–fold cross–validation with
accuracy as the performance measure to optimize. Using this approach we sub-
mitted 2 run files: SVR SVM.txt to the SVR subtask and CTR SVM.txt to the
CTR subtask.

3.4 Graph Classification using 2D CNNs
The second graph classification method used was based on the approach pro-
posed by Tixier et al. in [13]. Their original method was composed of four steps:
1. They first applied a graph node embedding called node2vec [19] that embeds
each node in a D–dimensional space (D is a priori different for each graph).
2. Since each graph Gk is described with a set of nk vectors of dimension Dk ,
where nk is the number of nodes in graph Gk , they used PCA to align the
dimensions of the embeddings and they selected the d first dimensions from
each embedding (where d < Dk , ∀k).
3. Then, they extracted 2D histograms by slicing the d–dimensional PCA node
embedding space. As a result, each graph is then represented as a stack of
d
2 2D histograms with size 28×28.
4. As a final step, they considered the 2D histograms as 2D images and they
trained a 2D CNN with input size ( d2 , 28, 28) to classify the initial graphs.
In our experiments we followed the same pipeline with d = 5 and we used
the same CNN architecture3 . However, since for each patient we extracted
four graphs (see Section 3.1), after step 3 we concatenated their respective
5–dimensional embeddings, obtaining 4 · 25 = 10 2D histograms. The net-
work used was then set up to have input size (10,28,28). With this tech-
nique we submitted two runs, one per subtask: SVR GNN node2vec pca.csv and
CTR GNN node2vec pca.csv.
A variant of the same method was also tested using the graph centrality
measures described in Section 3.3. In this case, we replaced the node2vec and
PCA steps by directly describing each node with the five abovementioned mea-
sures. The next steps were maintained, i.e. the concatenation of the embed-
dings, the creation of the 2D histograms of size 28×28 and the classification
using a 2D CNN with input size (10,28,28). Two more runs, one for each sub-
task, were submitted using this approach: SVR GNN nodeCentralFeats.csv and
CTR GNN nodeCentralFeats.csv.
Our last set of submitted runs was based on selecting the best chan-
nels before the CNN: For each binary problem we selected during training
the channels where the average 2D histograms differentiated stronger between
classes. This selection was done manually by visual inspection of the average
2D histograms for each problem and class and the number of selected chan-
nels varied for each binary CT–finding. Figure 2 shows the average 2D his-
tograms obtained for each embedding (based on graph centrality measures and
node2vec) for the SVR subtask. For the shown example we selected the channels
{2,4,7,9} in both embeddings. With this last approach we submitted four addi-
tional runs SVR GNN node2vec pca sc.csv, SVR GNN nodeCentralFeats sc.csv,
CTR GNN node2vec pca sc.csv and CTR GNN nodeCentralFeats sc.csv.
For all our experiments we used the same training scheme: 80–20% training
and validation split with early stopping based on the validation accuracy. All
the other parameters remained the same than in the original work [13].

4 Results
Tables 1 and 2 show the results obtained by our runs on the test set. The
ground truth labels of the test set were never released and we just reproduce
the results provided by the organizers of the ImageCLEF TB task here. In both
cases we included the best run for each subtask that in both cases is from the
UIIP BioMed group.

5 Discussion and Conclusions
In this work we applied our latest graph model of the lungs with morphology–
based structure to assess the TB severity score and to detect the presence of
3
https://github.com/Tixierae/graph_2D_CNN
Centrality measures node2vec
Class 0 Class 1 Class 0 Class 1

Channel 1
Feats:
{1,2}
Channel 2
Feats:
{3,4}
Channel 3
Feats:
{5,6}
Channel 4
Feats:
{7,8}
Channel 5

{9,10}
Feats:
Channel 6

{11,12}
Feats:
Channel 7

{13,14}
Feats:
Channel 8

{15,16}
Feats:
Channel 9

{17,18}
Feats:
Channel 10

{19,20}
Feats:

0 max

Fig. 2. Average 2D histograms over the training set for each SVR class obtained with
each embedding (graph centrality measures and node2vec). In this case, class 0 refers
to LOW and class 1 to HIGH TB severity.
Table 1. Results obtained in the SVR subtask for each submitted run.

Group name Run AUC Accuracy Rank
UIIP BioMed SRV run1 linear.txt 0.7877 0.7179 1
MedGIFT SVR SVM.txt 0.7196 0.6410 9
MedGIFT SVR GNN nodeCentralFeats sc.csv 0.6457 0.6239 26
MedGIFT SVR GNN nodeCentralFeats.csv 0.5496 0.4701 42
MedGIFT SVR GNN node2vec pca.csv 0.4933 0.4615 47
MedGIFT SVR GNN node2vec pca sc.csv 0.4076 0.4274 53

Table 2. Results obtained in the CTR subtask for each submitted run.

Group Name Run Mean AUC Min AUC Rank
UIIP BioMed CTR run3 pleurisy as SegmDiff.txt 0.7968 0.6860 1
MedGIFT CTR SVM.txt 0.6795 0.5626 5
MedGIFT CTR GNN nodeCentralFeats sc.csv 0.5381 0.4299 27
MedGIFT CTR GNN node2vec pca sc.csv 0.5261 0.4435 29
MedGIFT CTR GNN nodeCentralFeats.csv 0.5104 0.4140 31
MedGIFT CTR GNN node2vec pca.csv 0.5016 0.2546 33

six TB–related abnormalities on lung CT scans. Once the graph was built, two
approaches were performed to classify the lung graphs. It is worth to remember
that their comparison is not straight forward since the graphs contain a different
number of nodes and edges. Among the two classification techniques applied,
the CNN with the 2D node embeddings performed much worse than the SVM
classifier using the lung descriptor vectors (see Sections 3.3 and 3.4). However,
among the two node embeddings attempted there is no clear winner. Analyzing
the results obtained after reducing the number of channels (runs with sc suffix),
there is improvement in both subtasks only when using the embedding based on
the graph centrality measures. In any case, the results obtained with the CNN–
based classification are really low for both subtasks.
Considering our best approach, we ranked 6th and 3rd at the group level,
in the SVR and CTR subtasks respectively, with results clearly above random.
This supports the suitability of modeling the lungs as a graph for the proposed
subtasks. However, the results show that there is still room for improvement,
particularly in the CTR subtask. In the case of the SVR subtask, better results
could be obtained by using the provided meta–data or even the predicted CT–
findings. For the CTR subtask, we believe that using a classifier that could keep
the relations between the presence/absence of the diverse CT–findings will boost
our results.

Acknowledgements

This work was partly supported by the Swiss National Science Foundation in
the project PH4D (320030–146804).
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