# AI Radiologist: Revolutionizing Liver Tissue Segmentation with Convolutional Neural Networks and a Clinician-Friendly GUI

arXiv: https://arxiv.org/abs/2406.07688 · HTML: https://mlsanity.com/paper/2406.07688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 86 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observations](https://mlsanity.com/paper/1406.2639/) (2014)
- [3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes](https://mlsanity.com/paper/1607.00582/) (2016)
- [Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields](https://mlsanity.com/paper/1610.02177/) (2016)
- [RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans](https://mlsanity.com/paper/1811.01328/) (2018)
- [3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes](https://mlsanity.com/paper/1711.08580/) (2017)
- [Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks](https://mlsanity.com/paper/1702.05970/) (2017)
- [Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method](https://mlsanity.com/paper/1704.07239/) (2017)
- [Scheduling Techniques for Liver Segmentation: ReduceLRonPlateau Vs OneCycleLR](https://mlsanity.com/paper/2202.06373/) (2022)
- [Automatic Liver Segmentation Using an Adversarial Image-to-Image Network](https://mlsanity.com/paper/1707.08037/) (2017)
- [Automatic 3D liver location and segmentation via convolutional neural networks and graph cut](https://mlsanity.com/paper/1605.03012/) (2016)
- [Hierarchical Convolutional-Deconvolutional Neural Networks for Automatic Liver and Tumor Segmentation](https://mlsanity.com/paper/1710.04540/) (2017)
- [Liver segmentation and metastases detection in MR images using convolutional neural networks](https://mlsanity.com/paper/1910.06635/) (2019)
- [SAR-U-Net: squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography](https://mlsanity.com/paper/2103.06419/) (2021)
- [Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations](https://mlsanity.com/paper/2103.06384/) (2021)
- [Holographic Visualisation of Radiology Data and Automated Machine Learning-based Medical Image Segmentation](https://mlsanity.com/paper/1808.04929/) (2018)
- [Comparative performance analysis of the ResNet backbones of Mask RCNN to segment the signs of COVID-19 in chest CT scans](https://mlsanity.com/paper/2008.09713/) (2020)

## Abstract (arXiv)

Artificial Intelligence (AI) is a pervasive research topic, permeating various sectors and applications. In this study, we harness the power of AI, specifically convolutional neural networks (ConvNets), for segmenting liver tissues. It also focuses on developing a user-friendly graphical user interface (GUI) tool, "AI Radiologist", enabling clinicians to effectively delineate different liver tissues (parenchyma, tumors, and vessels), thereby saving lives. This endeavor bridges the gap between academic research and practical, industrial applications. The GUI is a single-page application and is designed using the PyQt5 Python framework. The offline-available AI Radiologist resorts to three ConvNet models trained to segment all liver tissues. With respect to the Dice metric, the best liver ConvNet scores 98.16%, the best tumor ConvNet scores 65.95%, and the best vessel ConvNet scores 51.94%. It outputs 2D slices of the liver, tumors, and vessels, along with 3D interpolations in .obj and .mtl formats, which can be visualized/printed using any 3D-compatible software. Thus, the AI Radiologist offers a convenient tool for clinicians to perform liver tissue segmentation and 3D interpolation employing state-of-the-art models for tissues segmentation. With the provided capacity to select the volumes and pre-trained models, the clinicians can leave the rest to the AI Radiologist.