Radiologists spend 15–20 minutes annotating a single MRI scan. With a flow of 40 scans per day, this turns into pure wasted time on routine. We develop AI systems that segment brain tumors, analyze white matter structure, and build tractograms in minutes with Dice 0.85–0.92. Without engineering experience, you risk getting a model that 'fails' on data from a neighboring scanner due to domain shift. Contact us for a preliminary data audit — it's free when signing a contract.
Why is AI analysis of MRI more difficult than CT?
MRI does not have a single intensity scale, unlike HU for CT. The signal depends on the specific scanner, coil, and protocol. Normalization is a critical step. We use Z-score on brain tissue, resampling to isotropic resolution 1 mm³, and central crop to 128³. This guarantees consistent segmentation quality regardless of equipment manufacturer (Siemens, GE, Philips).
What risks are there when implementing AI in MRI and how to avoid them?
The main problem is data annotation. MRI annotation is labor-intensive, and inconsistencies between experts are common. We use the STAPLE methodology (Warfield et al., 2004) to combine annotations and train the model on an ensemble. This increases Dice by 1–3% and reduces the risk of overfitting on poor examples. The second risk is data drift: new scanners or protocols can degrade quality. We implement metric monitoring in production and fine-tune the model if necessary.
How do we ensure segmentation accuracy?
Our stack includes PyTorch, MONAI, SwinUNETR, and FastSurferCNN. For the BraTS task, we achieve Dice: ET 0.85, TC 0.87, WT 0.90. Our model surpasses FreeSurfer in speed by ~100x (1–5 minutes vs. 6–8 hours) with comparable accuracy. We also use LoRA for fine-tuning on specific datasets and INT8 quantization for inference on edge devices. Unlike off-the-shelf solutions, our pipeline is 30% faster thanks to TensorRT optimization.
import numpy as np class MRIPreprocessor: def __init__(self, target_shape=(128,128,128)): self.target_shape = target_shape def normalize_zscore(self, volume: np.ndarray) -> np.ndarray: brain_mask = volume > 0 mean = volume[brain_mask].mean() std = volume[brain_mask].std() return (volume - mean) / (std + 1e-8) Validation Details
We use cross-validation by patients, metrics Dice, Hausdorff Distance, and sensitivity/specificity. All experiments are logged in W&B.What does the MLOps approach give?
We automate the training, validation, and deployment cycle: metric logging in W&B, data versioning via DVC, A/B testing of models. This guarantees reproducibility and fast rollout of updates. Compare: a typical team spends 3 months on a manual pipeline, we take 4 weeks from scratch.
| Parameter | FreeSurfer | FastSurfer | Our model |
|---|---|---|---|
| Brain segmentation time | 6–8 hours | 1–5 minutes | 1–2 minutes |
| Dice (cortical structures) | 0.88 | 0.90 | 0.91 |
| GPU required | No | Yes (NVIDIA V100+) | Yes (NVIDIA A100) |
Process of work
- Analytics: requirements gathering, data audit (format, modalities, annotation quality).
- Preprocessing: normalization, resampling, augmentation.
- Model development: architecture selection (U-Net, SwinUNETR), training on your data with early stopping.
- Testing: validation on held-out test, calculation of Dice, Hausdorff Distance metrics.
- Deployment: containerization (Docker), API on FastAPI, integration with PACS via DICOMweb.
- Support: documentation, training radiologists, 12-month performance guarantee.
What is included in the work
- Preprocessing and standardization of MRI series (DICOM → NIfTI, skull stripping, bias field correction).
- Model training and validation (PyTorch, MONAI) with metric logging in W&B.
- Model export to ONNX Runtime + TensorRT for acceleration.
- REST API for inference with authorization and logging.
- Integration with PACS (Orthanc/OHIF) via DICOMweb.
- Documentation (model card, API reference) and training of three key specialists.
Timelines and our experience
| Task | Estimated timeline |
|---|---|
| Segmentation of one organ (brain, knee) | 10–14 weeks |
| BraTS-compatible system (4 modalities) | 14–22 weeks |
| Multi-modal, multi-task analysis | 24–36 weeks |
We are a team of AI/ML engineers with years of experience in medical imaging. We have completed over 15 projects on automating MRI analysis for clinics from Russia and Europe. We hold certifications in MONAI and TensorRT. Automating MRI analysis reduces diagnostic costs up to 40% by speeding up radiologists' work — average savings of 2 million rubles per year per department. Additionally, reducing interpretation time can save up to 1.5 million rubles annually on licenses. Request a consultation to discuss your task.