<aside> 💡 Summary I developed a robust multi‑organ segmentation pipeline for CT images, enabling automated delineation of major organs to streamline diagnostic workflows. Leveraging advanced deep learning frameworks and systematic preprocessing, the pipeline yields consistent organ contours across diverse CT scans. The resulting models are integrated into our in‑house tools, enhancing the efficiency and accuracy of radiologists and enabling better visualization for disease diagnosis and treatment planning

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Background

Precise multi‑organ segmentation is essential for computer‑aided diagnosis, surgical planning, navigation and radiotherapy. Traditional thresholding, graph‑cut and region‑growing methods struggle with the large variations in organ size and shape and the noise present in CT images. Deep learning has emerged as a superior approach, greatly outperforming earlier methods and reducing the burden of manual annotation. Nevertheless, challenges such as the accurate segmentation of small organs and ambiguous boundaries in low‑contrast scans remain.

Approach

  1. Data Collection & Preprocessing: We curated a diverse CT dataset across institutions and various imaging devices to minimize bias. Preprocessing included intensity normalization, and noise reduction.
  2. Modeling Strategy: To capture organ‑specific features, we designed a deep learning network capable of learning multi‑scale anatomical patterns. Such networks automatically learn complex features from large datasets and thus outperform hand‑engineered methods.
  3. Custom Loss Design: Because organs vary greatly in volume, naive training tends to favour larger structures and overlook smaller ones. To mitigate this class imbalance, we devised a composite loss function that assigns different weights to each organ class based on relative volume, ensuring that small organs contribute meaningfully to the optimization process. This weighting strategy improved convergence and balanced the model’s attention across all target organs.
  4. Training & Validation: Cross‑validation ensured generalization, while data augmentation mitigated class imbalance. Dice scores and per‑organ accuracy were used to monitor performance across both large and small organs.

Note: Specific network architectures, loss function, hyperparameters and labeling strategies are not disclosed due to non‑disclosure agreements.

Results

Although quantitative metrics cannot be shared, the models consistently delineated both large (e.g., heart, liver, kidneys) and smaller organs (e.g., pancreas, spleen) with high reliability. Compared with traditional techniques, the models improved boundary clarity and processing time. They performed robustly across CT scanner and voxel spacing, and generalized well to new patient data. Feedback from internal medical imaging specialists confirmed the clinical utility of these segmentation results.

Conclusions

The developed segmentation model and loss function are now embedded in our in‑house medical imaging interpretation tool, allowing clinicians to quickly visualize organ structures and more accurately detect pathologies. Through continuous feedback loops, we refine the models and preprocessing pipelines to improve performance further. Future directions include expanding dataset diversity, exploring explainable AI techniques and integrating multi‑modal data (CT, MRI) to maximize clinical benefit.