=Paper=
{{Paper
|id=Vol-3333/paper11
|storemode=property
|title=Position Paper: Towards an automatic approach based on MAPE-K for the registration of multi-modal data in medical imaging
|pdfUrl=https://ceur-ws.org/Vol-3333/Paper11.pdf
|volume=Vol-3333
|authors=Marwa Chaabane,Bruno Koller
|dblpUrl=https://dblp.org/rec/conf/tacc/0002K22
}}
==Position Paper: Towards an automatic approach based on MAPE-K for the registration of multi-modal data in medical imaging
==
https://ceur-ws.org/Vol-3333/Paper11.pdf
Position Paper: Towards an automatic approach based
on MAPE-K for the registration of multi-modal data in
medical imaging
Marwa Chaabane1,2,* , Bruno Koller2
1
Department of Computer Science, University of Kiel, Germany
2
Scanco Medical AG, 8306 Brüttisellen Switzerland
Abstract
Nowadays, combining the medical data is important to improve the medical analysis. Several work
are interested in enhancing the image registration process in terms of accuracy and CPU consumption.
However, the imaging data is more and increasing and heterogeneous which require manual complex
intervention. Our work aim to perform the registration process as automatically as possible on a large
set of imaging data based on MAPE-K.
Keywords
MAPE-K loop, Fully automatic registration, Medical image, Multi-modal images registration
1. Introduction
In the medical imaging field, there is a huge amount of medical images generated to perform
several analysis [1] [2].
Our work is expected to use a large amount of 2D and 3D images from different modalities
such as micro Computed Tomography (CT), Computed Tomography (CT), Magnetic Resonance
Imaging (MRI), Positron Emission Tomography (PET), UltraSound and PhotoAcoustic (USPA)
and histological sections. We aim to combine these images and to describe their relative relation
in space and time with a multi-modal image registration process .
The collected images for the registration process are heterogeneous in terms of color systems,
gray levels, resolutions and dimensions. Also, they may be provided by different scanners in
several institutes [3]. In fact, two scans with the same modality but provided by two different
scanners may have different gray intensities.
Thus, the multi-modal image registration is a complex task and it requires several manual
interventions by the user/expert of the domain to adjust the image registration parameters
properly to the characteristics of the processed imaging data.
In literature, several studies are interested only on the registration algorithm enhancement like
enhancing the accuracy of the image matching, improving the computing time, etc. Nevertheless,
TACC’22: The 2nd Tunisian-Algerian Joint Conference on Applied Computing, December 13-14, 2022, Constantine,
Algeria
*
Corresponding author.
$ mchaabane@scanco.ch (M. Chaabane); bkoller@scanco.ch (B. Koller)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
each provided solution is dealing with only one modality combination and don’t address the
heterogeneity, diversity of modality combinations and big amount of data, in the registration
process.
These solutions require an important effort of the user/expert. The expert needs to usually
perform several parameters adjustment manually to ensure the proper functioning of the
registration process for each type of modality combination.
In our work, we propose a novel architecture for a fully automatic multi-modal registration
process without the expert intervention. The automatic multi-modal registration process can
be applied in different modality combination and address a big amount of heterogeneous data.
This architecture is based on a MAPE-K loop [4] inspired by the architecture of autonomous
systems [5].
The rest of the paper is organized according the directives of presenting a position paper [6]
as follows: Section 2 presents the opinion of studies which think that the expert intervention is
essential to any images registration process. Section 3 introduces our work which presents an
approach of fully automatic registration process to address heterogeneity and big amout of data
in the registration process. The conclusion is reported on Section 4.
2. Counter Argument
In literature, there are different studies addressing medical images registration process [7][8]
[9][10][11]. Each registration process handles only a specific modality combination and data-set
owning the same characteristics (same color system, same resolution,..).
Nevertheless, several modality combinations are needed to be provided to perform some
indispensable medical analysis.
Thus, the experts of the domain need to combine two different inputs from two different
modalities.
So, the experts needs to perform the following processes manually:
• Providing registration parameters: pixel-size, segmentation threshold,etc
• Deciding the needed pre-processing: converting, filters, segmentation,etc
• Deciding the registration workflow: 1) converting, 2) segmenting, 3) registering
• Managing the data scan by scan
The user manual interventions can be divided into 2 main categories :
• Setting parameters (to deal with heterogeneity)
• Deciding registration workflow ( to deal with the divers modality combinations and big
amount of data)
The existing solutions in literature give great importance for the manual interventions
performed by the expert, especially when we need to combine two different images from tow
different modalities.
They focus only on the registration algorithm enhancement, to improve the computing time
and the accuracy of the matching. But there is almost no work handling with the whole process
of the registration which requires an important effort by the expert.
3. Arguments
Due to the characteristics of the medical imaging data, the expert has to make a huge manual
effort to manage this data and perform image registration. The manual intervention of the expert
for collecting and selection of the appropriate parameters according to the different/diverse
modalities, structure, color system and the decision of the workflow steps are time consuming
and complex task.
Thus, our aim is to perform the registration in a dynamic way, taking into account the
characteristics of the medical images with the minimum of manual interventions. To fulfill this
need, we aim to provide a fully automatic registration process for big amount of heterogeneous
medical images. To achieve these objectives we decided to bring the solution from the software
architecture field [12, 13], based on the work in literature that are interested in eliminating the
manual user intervention, namely the autonomous systems using MAPE-K loop [4].
In our work, we proposed an automatic registration process, inspired from MAPE-K loop. In
the following, we detail its four steps illustrated in Figure 1, namely, Monitoring (M), Analysis
(A), Planning (P) and Execution (E) as well as the Knowledge module (K).
Figure 1: MAPE-K loop steps
The Monitoring phase (M) is responsible in collecting the relevant information about the
changing context of the system. In our approach the relevant information are the parameters
required in the different steps of the registration process.
In Analysis phases (A), the system makes the needed diagnosis based on the collected relevant
information [14]. Then, it plans possible plans of decision to be executed. In our approach the
possible plan of decision consists in a set of suggested registration workflow.
In the Execution phase (E), the system execute the most appropriate plan of decision.
The knowledge layer (K) is composed of a “Reference database", a “Rules database" and
“Behaviour history database". The “Reference database" contains the information about the
structure of the data and how it is stored each external database. The “Rules database" and the
“Behaviour history database" are connected to each others to store the behaviour of the system
and the information about the executed registration process.
Thus, in our approach, The expert intervention are replaced by the knowledge layer (K) based
on learning techniques.
4. Conclusion
Due to the increasing amount of imaging data in the medical domain and the heterogeneity
of these data, the registration process require complex manual tasks from the expert of the
domain. We propose a novel automatic approach to make the registration process automatically
on a large set of imaging data. Our proposed approach, based on MAPE-K loop aims to adapt
automatically the functioning of the solution according the changing characteristics of the
imaging data.
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