MHP-Net: A revolutionary AI model for accurate liver tumor segmentation for diagnosis and therapy

Liver cancer is the sixth most common cancer globally and a leading cause of cancer-related deaths. Accurate segmentation of liver tumors is a crucial step for the management of the disease, but manual segmentation by radiologists is labor-intensive and often results in variations based on expertise. Artificial intelligence (AI)-based tumor segmentation models have revolutionized tumor assessment in medical imaging—using deep convolutional neural networks, they identify and outline the exact shape, size, and location of a tumor in a medical scan image. But their efficacy comes with a heavy dependence on large volumes of data (typically ranging from 1,000 to 10,000 cases). This requirement for big data is a major barrier in medical AI. 

To overcome this barrier, a team of researchers led by Professor Kenji Suzuki and a PhD student, Yuqiao Yang, from the Biomedical AI Research Unit of Institute of Science Tokyo (Science Tokyo), Japan, has developed a groundbreaking AI model that can accurately segment liver tumors from computed tomography (CT) scans—even when trained using extremely small datasets— surpassing the performance of current state-of-the-art systems. Their study was published in Volume 13 of the journal IEEE Access on May 16, 2025.  

At the heart of this innovation is a novel architecture called the multi-scale Hessian-enhanced patch-based neural network (MHP-Net). MHP-Net works by breaking medical images into small 3D image patches—so the AI can focus on one part at a time rather than the entire image. It then pairs each patch from the original CT image with a corresponding enhanced version, achieved through a technique called Hessian filtering. Hessian filtering helps highlight spherical objects such as tumors in the image. 

The result is a high-resolution tumor segmentation map that accurately delineates liver tumors from contrast-enhanced CT scans. To evaluate the model’s performance, the team used the “Dice similarity score,” which compares how well the predicted segmentation matches the ground truth (usually annotated by expert radiologists) on a scale of 0 to 1. 

“Despite a limited training set of 7, 14, and 28 tumors, we achieved high performance dice scores of 0.691, 0.709, and 0.719, respectively,” notes Suzuki. “With these scores, our model surpasses major established models such as U-Net, Res U-Net, and HDense-U-Net.” 

Apart from its promising performance, the lightweight architecture of the model allows for fast training (under 10 minutes) and real-time inference (~4 seconds per patient), making it highly suitable for use even in clinical settings with limited computational resources. 

“This is just a start in the field of small-data AI, where meaningful and clinically relevant deep learning models can be built from limited datasets.” Says Suzuki. “MHP-Net’s success can inspire small-data AI solutions in other areas of medical imaging as well, such as the detection of rare cancers.” 

The study marks the potential of small-data AI in medical image analysis. By lowering the threshold for the data required for training, MHP-Net democratizes the use of AI in medical image analysis, especially in under-resourced hospitals and clinics with limited access to data. In the future, the researchers plan to explore broader applications of the small-data AI models—enabling scalable, cost-effective, and versatile deployment of AI in healthcare worldwide. 

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About Institute of Science Tokyo (Science Tokyo) 
Institute of Science Tokyo (Science Tokyo) was established on October 1, 2024, following the merger between Tokyo Medical and Dental University (TMDU) and Tokyo Institute of Technology (Tokyo Tech), with the mission of “Advancing science and human wellbeing to create value for and with society.”