3D Medical Image Segmentation for AI Model Training

When people discuss AI in medicine, images such as X-rays, computed tomography, or MRI immediately come to mind. However, there is a big difference between how the human eye sees these images and how AI must "see" them.

Medical images are never just flat 2D pictures. They are always a set of slices that, when stacked together, form the three-dimensional volume of the patient’s body. So, a medical scan always has depth.

This is why, for an AI model to work accurately, it must see the organ or pathology in a 3D volume. A tumor or an affected area is never just on one plane; it has a complex shape and extends across several slices. 3D segmentation is the process where a specific pathology is outlined in all three dimensions simultaneously.

When AI sees the object in volume, it significantly increases the accuracy of diagnosis and surgical planning. The system can not only find a tumor but also precisely measure its volume, shape, and distance to critical blood vessels.

Without quality 3D labeling, AI models work unreliably. If you train AI only on flat images, it might "lose" the pathology when it moves to the next slice, which is unacceptable in diagnosis.

Quick Take

• Segmentation creates a perfect 3D mask that serves as the gold standard of truth for training the AI model, indicating exactly where the target structure is located.
• Experts manually mark structures slice by slice on 2D images, which are then assembled by specialized tools into a full 3D mask.
• Any labeling must undergo double-checking by another doctor. This is a necessary quality control step, as an error in labeling will lead to incorrect AI diagnoses.
• Quality 3D segmentation ensures faster diagnosis, increased objectivity in measurements, and the standardization of clinical analysis.
• The workflow always includes the anonymization of DICOM files to ensure confidentiality.

What Exactly is Segmented on CT/MRI Scans

Medical 3D image segmentation encompasses a vast array of tasks. Depending on the goal, the AI model can be trained to isolate large, clear organs as well as tiny, blurry pathologies.

Objects of Segmentation by Structure Type

Segmentation focuses on isolating anatomical structures and pathologies, which is critically important for accurate diagnosis and treatment planning.

• Organs. This is one of the most common tasks in volumetric segmentation. It includes isolating large vital organs, such as the liver, lungs, heart, kidneys, and brain. Accurately outlining the organ's boundaries is necessary for measuring its volume, detecting shape abnormalities, and assessing its general condition.
• Vessels. This is a more complex task because vessels are thin and winding. Segmenting the vascular system helps diagnose aneurysms, stenoses, and plan complex cardiovascular and neurosurgical interventions.
• Pathologies. This is the most important, but also the most complex, category. It includes tumors, cysts, inflammation sites, or affected areas. Accurate tumor segmentation allows doctors to not only confirm the diagnosis but also measure its volume and monitor the effectiveness of chemotherapy.

Objects of Segmentation by Application

Some structures are segmented not for diagnosis, but for navigation and planning specific procedures.

• Anatomical Structures for Navigation. Individual bones or critical areas are segmented to serve as reference points for surgical robots or navigation systems. This allows the surgeon to accurately determine where to insert an instrument or make an incision.
• Fractures and Tissue Damage. Segmentation is necessary for isolating fracture lines on scans or determining the boundaries of damaged soft tissues. This helps orthopedists and trauma surgeons assess the degree of damage and plan reconstructive surgeries.
• Instruments or Implants. Surgical instruments or already installed implants are segmented during or after surgery. This allows for controlling their precise location and interaction with surrounding tissues.

How Experts Annotate 3D Medical Data

To train AI to "see" diseases, medical specialists must create a "gold standard" dataset. This process of 3D image annotation is incredibly time-consuming, but its quality is vital for the model's future accuracy.

From Slice to Volume

The process begins with the work of medical professionals, radiologists, or specially trained annotators. They do not work with a single whole 3D object, but with individual slices obtained during CT or MRI.

Experts manually mark the necessary structures on each slice separately. For example, the liver for organ detection or tumor boundaries for tumor segmentation. It is like drawing the contour of an object on hundreds of transparent films. During multi-organ annotation, the expert may sequentially isolate the lungs, heart, and then blood vessels on one slice at a time.

Mask Assembly and 3D Visualization

After the expert finishes labeling all slices, specialized tools assemble these contours together, forming a single, complete 3D mask. This stage is called volumetric segmentation.

The resulting 3D object allows for the creation of a 3D visualization. This enables the expert to visually assess how smoothly and correctly the structure is isolated in volume, detect irregularities that were previously unnoticeable in separate 2D images, and ensure that the object, such as a tumor, has no gaps.

Quality Assurance and Accuracy

Because this process creates the standard of truth for AI training, quality is the priority. Mandatory quality control steps are provided for this:

• Double Check. The mask created by one annotator is necessarily checked by a second expert. This reduces the risk of subjective error.
• Alignment and Correction. Manual alignment of contours is performed so that the object boundary is smooth between adjacent slices. Experts must also correct artifacts – noise or distortions caused by the device that could confuse the AI.

Any inaccuracy here will lead to the AI learning to make incorrect diagnostic conclusions, which can have too serious consequences.

Typical Workflows for Preprocessing 3D Medical Data

Preparing medical data for training AI models is a complex, multi-stage process that requires high accuracy and strict adherence to confidentiality rules. This workflow turns a patient's scan into a standardized dataset for machine learning.

Data Collection and Protection

The workflow always begins with obtaining the source medical images in the DICOM format. This is a standardized format that contains both the images themselves and metadata about the equipment, scanning parameters, and the patient.

All personal patient data contained in the headers of DICOM files is removed or replaced with random identifiers. This is necessary to ensure confidentiality and the possibility of using the data for research.

Processing and Segmentation

After data protection, the technical preparation of the images occurs. Medical scans obtained from different devices may have different quality, resolution, and contrast. At this stage, the data undergo normalization and are often converted into formats more convenient for AI, while preserving the volumetric structure.

Next, experts perform the segmentation of the target structures. They manually or semi-automatically outline the boundaries of organs, vessels, or tumors on each slice, creating the reference 3D mask. This mask is essentially the ideal key for training the AI model.

Finalization and Deployment

The last steps ensure the quality and readiness of the data for model training. The segmented data is necessarily subjected to a final check by another independent doctor or expert. The validator checks the accuracy and consistency of the contours, especially in "gray areas".

In the final step, the labeled images and their corresponding 3D masks are combined into a dataset. This dataset is ready to be loaded into the training environment, where the AI model will compare its own predictions with the ideal masks to learn to perform segmentation and detection accurately.

Final Practical Benefits for Hospitals and AI Teams

Quality 3D data segmentation is the foundation for implementing AI, bringing a number of real advantages that radically change clinical practice and scientific research.

Acceleration and Diagnosis Support

The main benefit lies in optimizing the work of medical staff:

• Diagnosis Acceleration. Models trained on accurate 3D segmentation can isolate organs and pathologies in a matter of seconds. This significantly speeds up the radiologist's work, freeing up time for more complex cases, and allows doctors to start patient treatment sooner.
• Support for Doctors and Radiologists. Models serve as a second opinion. They reduce the risk of missing a pathology, increasing the confidence of specialists when making decisions.

Creating a Reliable Foundation for AI

For teams developing AI solutions, segmentation provides the ability to create advanced tools.

Quality segmented data is the foundation for creating reliable AI models. Only models trained on the "gold standard" of 3D data can be permitted for use in responsible clinical practice.

A standardized data preparation process allows for combining scans from different hospitals and countries. This makes it possible to create large, diverse datasets necessary for training universal AI systems.

Increased Objectivity and Standardization

Quality labeling eliminates human subjectivity, making analysis more reliable:

• Standardization of Analysis. AI that works with clearly segmented data always performs measurements the same way. This eliminates subjectivity and human variability, ensuring the standardization of diagnostic reports across the entire clinic.
• Quantitative Analysis for Treatment. Segmentation allows for the precise measurement of the volume of a tumor or affected area. This objective quantitative data is important for assessing treatment effectiveness.

FAQ

What quality metrics are used to evaluate segmentation?

The dice coefficient is most often used to measure the accuracy of volumetric segmentation. This metric shows how much the AI model's prediction overlaps with the ideal 3D mask created by experts.

What neural network architecture is the most popular for 3D segmentation?

The most popular architecture is U-Net and its three-dimensional version, V-Net. These networks are specifically designed for segmentation tasks because they can effectively analyze both the overall image context and the precise boundaries of objects.

How long does manual segmentation of one 3D scan take?

Manual expert segmentation is extremely labor-intensive and expensive. Depending on the complexity of the structure, annotating one full 3D volume can take from several hours to several days of a highly qualified doctor's work.

How is the problem of huge 3D data volumes solved?

To work effectively with gigantic 3D volumes, AI models usually do not process the entire scan. Instead, patching techniques are used, where only a small fragment of the 3D image containing the target pathology is used to train the model.

Is regulatory certification required for AI trained on this data?

Yes. An AI model used for diagnosis or treatment in clinical practice is considered a medical device. It must undergo strict regulatory certification based on data proving its accuracy and safety, such as the FDA in the USA or the CE Mark in Europe.