Deep Learning for Medical Image Segmentation

Medical image segmentation is one of the most impactful applications of deep learning in healthcare. Accurate segmentation of anatomical structures — like the pancreas in CT scans — enables clinicians to detect tumors earlier, plan surgeries more precisely, and monitor treatment progress automatically.

In this post I'll share key insights from my research on automatic pancreas segmentation using a two-phase Mask R-CNN and 3D U-Net approach, published in Computer Methods and Programs in Biomedicine (2021).

Why Is Pancreas Segmentation So Hard?

The pancreas is notoriously difficult to segment automatically:

• Small and irregular shape — unlike the liver or heart, the pancreas has no consistent geometry
• Low contrast — it blends with surrounding fat and other soft tissue in CT
• High inter-patient variability — size and location vary dramatically between individuals

These challenges make classical algorithms (thresholding, region growing) essentially useless. Deep learning is the only realistic path forward.

The Two-Phase Approach

Our solution breaks the problem into two stages:

Phase 1 — Coarse Localization (Mask R-CNN)

First, we use Mask R-CNN to roughly locate the pancreas region in each CT slice. This dramatically reduces the search space and eliminates irrelevant background anatomy.

# Conceptual pseudocode
roi = mask_rcnn.detect_pancreas_region(ct_slice)
cropped_volume = extract_roi(full_ct_volume, roi, margin=20)

Phase 2 — Fine Segmentation (3D U-Net)

The cropped region is then fed into a 3D U-Net, which operates on volumetric patches and captures 3D spatial context — critical for a structure as thin and irregular as the pancreas.

# 3D U-Net processes the ROI volume
segmentation_mask = unet_3d.predict(cropped_volume)
final_mask = postprocess(segmentation_mask)

Key Results

Our approach achieved a Dice Similarity Coefficient (DSC) of ~85% on the NIH Pancreas-CT dataset — competitive with state-of-the-art methods at the time, with significantly reduced false positives compared to single-stage approaches.

Lessons Learned

1. Coarse-to-fine always helps for small structures — don't try to segment directly from full volume
2. 3D context matters — 2D slice-by-slice approaches miss inter-slice information
3. Data augmentation is essential — we applied random rotations, elastic deformations, and intensity shifts
4. Post-processing removes noise — connected component analysis and morphological operations clean up predictions

What's Next?

Current research directions include: - Foundation models (SAM, MedSAM) for few-shot medical segmentation - Multimodal learning — combining CT, MRI, and clinical notes - Interpretable AI — helping clinicians understand model decisions

I'm actively working on multimodal medical AI as part of my postdoctoral research at The Ohio State University, supported by a TÜBİTAK 2219 fellowship.

📄 Full paper: DOI: 10.1016/j.cmpb.2021.106141