Biomedical image processing offers numerous project opportunities that combine technical challenges with real-world impact. Three strong project ideas include automated tumor segmentation in MRI scans, cell counting and classification in microscopy images, and 3D reconstruction of anatomical structures from 2D scans. These projects address common needs in diagnostics and research while leveraging tools like Python, OpenCV, and deep learning frameworks. Developers can start with publicly available datasets and modular codebases to iterate quickly.
For tumor segmentation, a project could focus on training a convolutional neural network (CNN) to identify brain tumors in MRI scans. Using the BraTS dataset (a standard benchmark), developers can experiment with architectures like U-Net or Mask R-CNN to segment tumor regions. Key steps include preprocessing scans (normalization, skull-stripping), augmenting data to handle limited samples, and evaluating performance with metrics like Dice coefficient. Tools like PyTorch or TensorFlow simplify implementation, while libraries like MONAI offer domain-specific functions for medical imaging. This project teaches handling class imbalance (e.g., small tumor regions) and integrating domain knowledge (e.g., MRI modality differences).
Another project could involve automating cell counting in histopathology images. Using datasets like the Sartorius Cell Segmentation Challenge, developers can build a pipeline to detect and classify cells in brightfield microscopy images. A combination of traditional techniques (thresholding, watershed algorithms) and machine learning (YOLO for detection) works well here. For example, a hybrid approach might use OpenCV for initial cell region proposals and a lightweight CNN to filter false positives. Challenges include handling overlapping cells and varying staining intensities. This project is practical for clinics needing faster analysis of blood samples or tissue biopsies, and it can be extended to detect abnormal cells (e.g., cancerous vs. healthy).
A third project could focus on 3D reconstruction of organs from ultrasound or CT slices. Using public datasets like the NIH’s Visible Human Project, developers can align 2D slices into a 3D volume using registration algorithms (e.g., elastix/SimpleElastix) and render it with tools like VTK or PyVista. Applications include surgical planning or educational tools. A stretch goal could involve adding segmentation (isolating specific organs) or simulating tissue deformation. This project requires understanding spatial transformations, interpolation methods, and visualization techniques. It’s particularly valuable for integrating with AR/VR systems in medical training. All three projects balance algorithmic complexity with clear clinical relevance, making them ideal for developers seeking impactful work in biomedical imaging.