AI Development for MRI Analysis: Segmentation, Detection, MLOps

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)

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.

Process of work

1. Analytics: requirements gathering, data audit (format, modalities, annotation quality).
2. Preprocessing: normalization, resampling, augmentation.
3. Model development: architecture selection (U-Net, SwinUNETR), training on your data with early stopping.
4. Testing: validation on held-out test, calculation of Dice, Hausdorff Distance metrics.
5. Deployment: containerization (Docker), API on FastAPI, integration with PACS via DICOMweb.
6. 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

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.

How Distribution Shift Kills CV Model Metrics in Industry

On a production line, a camera is installed to control product quality. The model is trained on 10,000 labeled images—test accuracy mAP 0.84. Deployed to production, and in the first week it misses 30% of defects. Lighting on the line changes between shifts; distribution shift nullifies the metrics. This is a classic story with computer vision in industry, where pattern recognition fails without proper drift handling.

Our engineers, with experience from 60+ computer vision projects, know how to eliminate such scenarios. We guarantee stable model performance under real conditions.

Object Detection: YOLO, RT-DETR, and Everything in Between

YOLO is the standard for real-time detection. YOLOv8 and YOLOv11 from Ultralytics are the most used versions in production: simple API, active community, built-in validation, and export to ONNX/TensorRT. For tasks with high accuracy requirements and less critical latency, RT-DETR, a transformer-based architecture without NMS, gives better mAP on COCO at comparable speed to YOLOv8l.

A typical mistake when training a detector: dataset of 8000 images, 3 classes, fine-tune YOLOv8m—F1 0.73 on validation. Look at confusion matrix—one class is almost never detected. Cause: imbalance 1:23. Solution: oversampling rare class, focal loss for objectness, augmentations (Mosaic, MixUp disabled for rare class as they "blur" it). Transfer learning is mandatory: pretrained on COCO weights reduces data requirement by 10 times. Fine-tuning on 500–2000 domain images yields a working model in 1–2 days on a single GPU.

For edge deployment: export to ONNX → TensorRT engine. YOLOv8n in TensorRT FP16 on Jetson AGX Orin gives 150+ FPS at P99 latency < 8 ms—3 times faster than ONNX Runtime without TensorRT. On server A10G: 700+ FPS for YOLOv8n in TensorRT INT8.

How Does Fine-Tuning YOLO Help in Pattern Recognition?

Suppose you need to find micro-defects on a metal surface—a task with high resolution and class imbalance. We use YOLOv8m pretrained on COCO and fine-tune on 2000 proprietary images. Apply augmentations Mosaic, MixUp, random perspective. After 200 epochs, mAP 0.5 reaches 0.93. Key techniques:

• Focal loss for the objectness head—reduces contribution of easily classified examples.
• Class-balanced sampling—equalizes representation of rare classes.
• Test Time Augmentation (TTA)—increases recall by 5–7% through averaging over flips and scales.

Get a consultation on architecture selection for your task—contact us.

Segmentation: SAM, Mask R-CNN, and Instance Segmentation

SAM (Segment Anything Model) from Meta changed the approach to segmentation. SAM 2 works with video, supports object tracking across frames—for interactive object selection by point or bbox, it's the best out-of-the-box choice. For production instance segmentation without interactive prompting, Mask R-CNN or YOLOv8-seg are used. YOLOv8-seg trains like a regular detector with additional masks, convenient in the same pipelines. Semantic segmentation (each pixel is a class) uses SegFormer, DeepLabV3+. SegFormer-B5 provides a good balance of accuracy and speed for satellite imagery or medical segmentation.

Case study: cell segmentation on microscopic images. Dataset of 400 images with manual annotation. Training Mask R-CNN on ResNet-50 backbone gave IoU 0.61—poor. Problem: objects (cells) overlap; standard NMS kills overlapping predictions. Solution: switch to cellpose (specialized architecture for biomedical tasks) + soft-NMS. IoU increased to 0.79.

OCR: When Tesseract Fails

Tesseract is a starting point for simple tasks: printed text, good lighting, straight layout. As soon as there are handwritten elements, non-standard fonts, perspective distortions, or multi-column layouts, Tesseract degrades quickly.

PaddleOCR is a production-grade solution: text block detection + recognition + structural analysis. Works out of the box for 80+ languages, including Russian. Supports tables and complex document structures. TrOCR (Microsoft) is a transformer OCR with strong results on handwritten text. For Russian handwritten text, fine-tuning is needed: the base model is trained mostly on Latin script.

What to Do When Tesseract Cannot Handle Pattern Recognition on Documents?

For tasks like "extract data from invoices/contracts/passports," we use LayoutLMv3 or Donut—these models understand document layout, not just text. Integration via Hugging Face Transformers, fine-tuning on 200–500 annotated documents. Typical pipeline:

1. Preprocessing: deskew, denoising, binarization via OpenCV.
2. Text block detection: PaddleOCR detection or CRAFT.
3. Recognition: PaddleOCR recognition or TrOCR.
4. Post-processing: normalization, validation via regex or LLM for structured fields.

For documents with fixed structure, template matching + OCR by coordinates is often more reliable than an end-to-end solution.

Face Recognition: Identification and Verification

Face recognition = detection + alignment + embedding + matching. Each stage matters.

Detection: RetinaFace or InsightFace for accurate face localization and keypoints. MTCNN is older but reliable. Embedding: ArcFace (InsightFace) is state-of-the-art for face recognition embeddings. Models iresnet50/iresnet100 pretrained on MS1MV3 (5M identities). Embedding vector 512 float32, comparison by cosine similarity. Threshold tuning: decision threshold is a critical parameter. At threshold 0.6, typical FPR on LFW benchmark is 0.001, TPR is 0.985. In production, threshold must be calibrated to the real distribution: people in masks, with changed appearance, different lighting conditions. Liveness detection is mandatory: MiniFASNet—lightweight model on CPU; FaceX-Zoo contains several pretrained liveness detectors.

Video Analytics

Video is a sequence of frames plus a temporal dimension. A naive approach—detecting on every frame—is expensive.

Tracking: ByteTrack and BoT-SORT are the standard for multi-object tracking. They work on top of any detector, adding persistent IDs to objects across frames—enabling object counting, motion tracking, velocity.

Optimization: not every frame needs processing. For static scenes, detect every 5–10 frames, with tracking in between. For event detection (person entering a zone), background subtraction (OpenCV MOG2) serves as a lightweight pre-filter before neural detection. Action recognition: SlowFast, VideoMAE for action classification. Heavy models—for production use ONNX export + TensorRT or offline processing.

How to Measure Pattern Recognition Model Quality in Production?

Quality monitoring is key to MLOps. We track:

• Prediction confidence distribution.
• Share of low-confidence predictions (indicator of OOD data).
• Drift of input images via feature distribution (embeddings from backbone).

A drop in average confidence from 0.87 to 0.71 over a week is an early signal of distribution shift. NVIDIA Triton Inference Server recommends tracking these metrics via Prometheus. Our certified engineers set up monitoring and guarantee SLA for inference quality.

Deployment of CV Models

For online inference, we use Triton Inference Server (NVIDIA)—production standard for serving CV models. Supports TensorRT, ONNX, PyTorch, dynamic batching, multiple instances. REST and gRPC API. We guarantee stable operation under load.

Edge deployment: ONNX Runtime on ARM/x86 CPU. TensorFlow Lite for mobile devices. OpenVINO for Intel CPU/GPU/VPU—gives 2–3× speedup on Intel hardware compared to ONNX Runtime. After deployment, we hand over the model with documentation and train personnel.

What Is Included in the Work

Deadlines and Cost

A prototype detector on existing data takes 1–2 weeks. Production system with optimization for target hardware takes 4–8 weeks. Full cycle including data labeling (1000–5000 images) takes 2–4 months. Cost is calculated individually for each task. Typical savings from implementing a quality control system can be significant per production line.

We have been in the market for over 5 years and completed 60+ computer vision projects. We will evaluate your project end-to-end—request a consultation to get a quote and technical proposal.