3D segmentation for patient-specific solutions, now with AI

Key takeaways
  • 3D segmentation is crucial in creating personalized solutions for surgeries, like 3D-printed implants and surgical guides.
  • It starts with high-quality medical scans, such as X-rays, CT scans, or MRIs, to create detailed 3D anatomic models.
  • In the past, this process involved a lot of manual work, which was time-consuming and prone to errors.
  • AI machine learning algorithms can make 3D segmentation faster, more accurate, and cost-effective, benefiting surgeons, patients, and medical device companies.

3D segmentation plays a critical role in the process of designing patient-specific solutions like 3D-printed implants and surgical guides.

Traditionally, this process was labor-intensive and prone to errors, but now, artificial intelligence (AI) and machine learning are taking the stage, offering efficiency and precision. 

AI-powered 3D planning platforms offer immense potential benefits for surgeons, patients, and medical device companies. In this blog, let’s explore the role of 3D segmentation and how AI is reshaping the landscape.

What is 3D segmentation?

3D segmentation is one of the first steps in the process of creating 3D-printed solutions. 

In healthcare, for example, when patient medical scans are uploaded, specialized software called 3D segmentation and visualization tools carefully analyze the 2D images, picking out essential details and anatomical landmarks. 

Using this data, the 3D segmentation platform creates highly detailed 3D models of the patient's body. The 3D models target and highlight specific structures like bones, joints, bony landmarks, and more. 


How does 3D segmentation work?

Here’s the 3D segmentation process to create patient-specific solutions for surgery. These include 3D-printed implants, surgical guides, other instrumentation, and 3D anatomical models.

Step 1: Getting the right patient scans

The first step in the segmentation process is acquiring high-quality medical imaging data. 

CT scans are often preferred for creating detailed 3D reconstructions of bony structures. However, MRI scans provide valuable information about soft tissues and organs and can also be used. 

New advancements in 3D segmentation can even generate 3D models using X-rays. 

Step 2: Image extraction

Once the right patient scans are obtained, advanced 3D imaging software extracts the relevant anatomical structures. 

Until recently, this extraction process had a significant manual component. In manual segmentation, a trained professional identifies and outlines the structures, and semi-automated or automated algorithms complement their work to enhance efficiency and accuracy.

The main goal of this step is to identify the correct landmarks, note distances between important anatomical landmarks, and mark the boundaries of different regions. 

For example, in a knee joint, the 3D segmentation software must:

  • Outline the shape of the bones
  • Distinguish cartilage versus bone
  • Detail irregularities from wear and tear that are specific to each patient and
  • Mark all associated ligaments.

Latest 3D segmentation platforms use AI to generate 3D models automatically.

Enhatch’s 3D planning portal allows you to scale your personalized solutions effortlessly.

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Benefits of accurate 3D segmentation

One of the main advantages of patient-specific implants is their ability to address anatomical irregularities that vary from one patient to another. Some of these variations can make surgical procedures more difficult without accurate visualization. 

The 3D segmentation process allows surgeons to visualize and understand these variations before the actual procedure, which enhances preoperative planning.

Surgeons can explore the virtual 3D models to identify potential complications, plan optimal implant placement, and anticipate any issues that may arise during surgery.

These 3D models offer in-depth information about the dimensions, form, and positioning of anatomical structures. This helps generate more accurate customized implants and surgical solutions designed specifically for each patient’s needs.


Challenges with manual segmentation

Performing the 3D segmentation process manually poses several challenges.


Manual segmentation is a labor-intensive process that requires skilled professionals to meticulously outline and delineate anatomical structures from medical imaging data. 

This process can be time-consuming, especially when dealing with intricate structures. As a result, manual segmentation can extend the overall timeline by several weeks, which can be quite cumbersome for surgeons and patients when preparing for their surgeries.

Variability in results

When segmentation is performed manually, interpreting medical images and identifying anatomical structures can be subjective, based on the individual engineer. This leads to variability in results, which may introduce inconsistencies in the final 3D model and patient-specific solutions.

Can be prone to human error

A manual process is prone to human errors that may directly impact the quality of the final 3D model and 3D-printed solutions. This can also add time to the overall process because more corrections may be required.  

Hard to identify complex anatomical structures 

Some anatomical structures may be intricate or exhibit subtle variations, which may be challenging to identify with the human eye alone.

Higher costs and more resources needed

Having to employ skilled professionals for manual 3D segmentation is expensive. Adding in costs for training and continuous improvement, it can be challenging for companies to rapidly scale their patient-specific portfolio of products.

In a highly competitive field like medical devices, larger companies may have the resources to employ larger teams for manual 3D segmentation. However, smaller to mid-sized companies face significant challenges catering to the market demand for patient-specific implants and surgical solutions. 


Benefits of AI in 3D segmentation 

As technology advances, artificial intelligence (AI) and machine learning algorithms are increasingly integrated into the segmentation process. 

These tools have the potential to streamline and automate critical aspects of the 3D segmentation process. 

With suitable AI-based algorithms, 3D segmentation platforms continue to learn, improve, and enhance their accuracy and performance, streamlining the entire process.

About the Enhatch planning portal

Enhatch’s 3D planning portal is powered by AI. 

Using proprietary machine learning algorithms, our platform converts 2D X-ray images to 3D anatomic models seamlessly. Our optimization algorithms further refine and fine-tune how surgical instruments and implants are positioned on anatomical landmarks. 

Want to know more about our new cloud-based, FDA 510(K) cleared, streamlined preoperating 3D planning portal?

An FDA-approved surgical planning solution for patient-specific solutions.
A cloud-based platform to use from any laptop or tablet—no downloads or lengthy installations required.
Executive dashboard to manage all patient-specific cases from one place, and much more.
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