Sepand AliMadadSoltani
sepand.alimadadsoltani@etu.univ-lyon1.fr | https://sepandsoltani.github.io
Education
Master II in Medical Device Engineering Lyon, FrancePolytech Lyon, Claude Bernard University Lyon 1 Fall 2024-Present
GPA: 14.12/20
Courses: Magnetic Resonance Imaging, Segmentation & Registration, ML/AI in Medical Images, Image Reconstruction & Inverse Problems
Bachelor of Science in Electrical Engineering (Bac+4) Tehran, Iran
K.N. Toosi University of Technology 2018-2023
Concentration: Biomedical Engineering
GPA: 16.33/20 (Last two years: 17.29/20)
Courses:Statistical Pattern Recognition, Signals & Systems
Honors and Awards
- €10,000 Excellence Scholarship awarded for excellent academic background by the Medical Device Engineering Graduate School program at Polytech Lyon
Research Interests
- Magnetic Resonance Imaging (MRI)
- Positron Emission Tomography (PET)
- Functional Imaging
- Neuroscience
- Machine Learning and Artificial Intelligence in Health
Research Experience
M2 Internship: Estimation of Image-Derived Input Function in Hybrid MRI and Dynamic Brain PET Imaging Lyon, FranceCERMEP November 2024-August 2025
- Conducted a state-of-the-art literature review
- Conducted pre-processing steps on MRI and PET images to ensure data quality and consistency
- Developed a robust segmentation algorithm for accurate extraction of internal carotid arteries from MR angiography images in python
- Implemented different Partial Volume Correction algorithms for obtaining accurate input function
- Evaluated method performance across multiple datasets using various PET quantification models
- Conducted Monte Carlo PET simulations for evaluation of the proposed methods
- Developed new tools and improved existing ones for fast and efficient processing of PET and MRI data in C and Python
Machine Vision & Medical Image Processing Laboratory (MVMIP), KNTU January-June 2023
- Developed a Python-based medical image analysis software, from scratch utilizing Python, VTK, and PyQt libraries
- Implemented multiple interactive tools (ruler, shapes, and text insertion tools)
- Developed an image processing algorithm for tissue boundary detection and integrated it in a smart interactive scissor tool for fast semi-automatic tissue segmentation
- Enabled users to import custom plugins to extend the functionality of the software based on their needs
- Successfully shipped the software for Linux and Windows operating systems
Skills
- Programming: Python, C, C++, CMake, Bash, MATLAB, QML
- Software and Tools: GNU/Linux, Git, FMRIB FSL, 3D Slicer, NiftyReg, dcm2niix, TPCCLIB
- Libraries: Tensorflow, PyTorch, NumPy, pandas, scikit-learn, Matplotlib, ITK, VTK, Qt, PyQt
- Languages: Persian (Native), English (TOEFL: Overall: 101/120, R: 27, L:27, S:23, R:24), French (Beginner-A2) </ul>
- Designed and developed a modern interface using the Qt Framework's QML language
- Built and optimized backend logic in C++ to handle large volumes of data efficiently
- Created a Python-based medical analysis software focusing on user-friendliness and user experience
- Designed and implemented a workflow user interface for bioinformatics analysis and processing using the Qt framework
- Worked with a team of engineers to integrate various machine learning algorithms into the workflow
- Designed and integrated a medical image visualizer using VTK
- Integrated multiple visualization tools and pipelines such as colormaps, image thresholding and interactive segmentation
- Implemented smart presence detection and remote-control support for the monitor stand in Valiasr Street Museum
- Implemented the network based on a paper using the Tensorflow library in Python
- Synthesized images of Persian text of different variety
- Applied data augmentation techniques such as rotating, translating, adding distortion, and adding noise to images
- Successfully trained the model for both languages using the self-made synthesized Persian dataset and public English datasets
- Achieved +85% accuracy for both languages
- Pre-processed and processed raw fMRI and MRI data from the ADNI database using the FSL library to extract time-series data to calculate functional connectivity maps of the subjects' brains
- Studied the previous works on this subject to find the gap
- Experimented with RCNN & CNN networks using Tensorflow to extract temporal and spatial features from images
- Gained hands-on experience with image pre-processing, neural network architecture, and deep learning principles
- Although a full model was not achieved, a lot of experience and insight were gained into medical imaging and deep learning concepts deep learning concepts
- Implemented brain extraction from structural reference MR image
- Implemented fMRI pre-processing including motion correction, slice timing correction, spatial smoothing, and co-registration
- Implemented atlas-based ROI time-series extraction
- Enabled parallel processing to accelerate computation for large datasets
- Utilized the program for processing fMRI data from the ADNI dataset
- Developed a custom 2D graphics renderer completely from scratch using the OpenGL graphics API in C++
- Implemented user input handling, navigatable menus, and text rendering capabilities to the engine
- Designed and implemented the game of Tetris using the said engine in Object Oriented C++
Work Experience
NTH Tehran, IranJunior C++ & QML Developer Oct 2023-July 2024
Medical Image Visualization Software (Freelance Project) Summer 2023
Electronics Engineer Internship Summer 2021