Synopsis
Brain tumors are one of the most challenging medical conditions to diagnose and treat due to their complexity and variability. Magnetic Resonance Imaging (MRI) is a widely used imaging modality for detecting and characterizing brain tumors, providing detailed structural information about the brain. However, manual segmentation of brain tumors from 3D MRI scans is time-consuming, prone to human error, and requires expert radiologists. Automating this process using deep learning techniques can significantly improve the accuracy and efficiency of tumor detection and segmentation. We aim to develop a deep learning-based pipeline for 3D MRI segmentation to accurately detect and delineate brain tumors. The model will leverage advanced architectures like 3D Convolutional Neural Networks (CNNs) or U-Net variants to process volumetric MRI data and identify tumor regions. This automated approach will assist in early diagnosis, treatment planning, and monitoring of brain tumor progression.
Relevance of the Topic
The accurate segmentation of brain tumors from MRI scans is critical for diagnosing and treating patients with brain cancer. Early and precise detection can lead to better patient outcomes by enabling timely intervention and personalized treatment plans. Furthermore, automating this process reduces the burden on radiologists, allowing them to focus on more complex tasks. With the increasing availability of large-scale medical imaging datasets and advancements in deep learning, there is a growing opportunity to develop robust models that can generalize across different types of brain tumors and imaging protocols.
Future Research/Scope
- Multi-modal MRI Integration: Future work could involve integrating multiple MRI modalities (e.g., T1-weighted, T2-weighted, FLAIR) to improve segmentation accuracy.
- Generalization Across Datasets: Investigate the model's ability to generalize across different hospitals and imaging protocols, addressing challenges related to dataset bias.
- Real-time Segmentation: Develop lightweight models capable of real-time tumor segmentation for use in clinical settings.
- Explainability: Incorporate explainability techniques (e.g., attention maps, and SHAP values) to provide insights into the model's decision-making process, which is crucial for gaining trust from medical professionals.
- Longitudinal Analysis: Extend the model to track tumor growth or shrinkage over time, aiding in treatment monitoring and prognosis prediction.
Skills Learned
- Deep Learning: Hands-on experience with 3D CNNs, U-Net architectures, and other segmentation models.
- Medical Image Processing: Understanding of MRI data preprocessing, including normalization, skull stripping, and data augmentation.
- Python Programming: Proficiency in Python and libraries like TensorFlow, PyTorch, and Numpy for implementing deep learning models.
- Data Visualization: Skills in visualizing 3D MRI data and segmentation results using tools like Matplotlib, Plotly, or ITK-SNAP.
Relevant courses to the topic
- CSE428: Image Processing
- CSE422: Artificial Intelligence
- CSE425: Neural Networks
- CSE427: Machine Learning
- CSE443: Bioinformatics I
- Coursera: AI for Medical Diagnosis
- Crash Course on Interpretation of Brain Imaging: Brain Imaging
Reading List/Study Materials
- Books
- "Deep Learning for Medical Image Analysis" – Zhou, Greenspan, Shen (Link)
- "Deep Learning for Biomedical Data Analysis" – Mourad Elloumi (Link)
- Research Papers
- "A Review of Deep-Learning-Based Medical Image Segmentation Methods" – Liu et al., Sustainability
Link - "U-Net: Convolutional Networks for Biomedical Image Segmentation" – Ronneberger et al., MICCAI
Link - "3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation" – Özgün Çiçek et al., MICCAI
Link - "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation" – Isensee et al., Nature Methods
Link - "Attention U-Net: Learning Where to Look for the Pancreas" – Oktay et al., MIDL
Link - "Image Segmentation Using Deep Learning: A Survey" – Minaee et al., IEEE Transactions on Pattern Analysis and Machine Intelligence
Link - "Deep learning for medical image segmentation: State-of-the-art advancements and challenges" – Rayed et al., Informatics in Medicine Unlocked
Link - "A Joint Graph and Image Convolution Network for Automatic Brain Tumor Segmentation" – Camillo Saueressig et al., MICCAI Workshops
Link | Code - "UnSegGNet: Unsupervised Image Segmentation using Graph Neural Networks" – Reddy et al., ArXiv
Link | Code - "VIG-UNet: Vision Graph Neural Networks For Medical Image Segmentation" – Jiang et al., ISBI
Link - "On the use of GNN-based structural information to improve CNN-based semantic image segmentation" – Coupeau et al., Journal of Visual Communication and Image Representation
Link - "Review of Graph Neural Networks for Medical Image" – Jing Wang, EAI Endorsed Transactions on e-Learning
Link
- Datasets
- BraTS 2021 (Brain Tumor Segmentation Challenge Dataset)
Link - BraTS 2020 (Brain Tumor Segmentation Challenge Dataset)
Link - BraTS 2023 (Brain Tumor Segmentation Challenge Dataset) [Updated from 2021]
Link - BraTS 2019 (Brain Tumor Segmentation Challenge Dataset)
Link - Decathlon Dataset (Task 01: Brain Tumor Segmentation)
Link - Kaggle: Brain MRI Segmentation Dataset
Link
- Code Tutorials & Repositories
- Videos & Playlists
