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Related Experiment Video

Updated: Dec 28, 2025

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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Published on: April 14, 2014

Novel Volumetric Sub-region Segmentation in Brain Tumors.

Subhashis Banerjee^1,2, Sushmita Mitra¹

¹Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India.

Frontiers in Computational Neuroscience

|February 11, 2020

Summary

A novel deep learning model, Multi-Planar Spatial Convolutional Neural Network (MPS-CNN), accurately segments brain tumor sub-regions from MRI scans. This automated approach shows high performance in identifying enhancing tumor and tumor core regions.

Keywords:

brain tumor segmentation class imbalance conditional random field convolutional neural network multi-planar CNN spatial-pooling and unpooling

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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

Published on: January 7, 2019

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Accurate segmentation of brain tumors and their sub-regions from multimodal MRI is crucial for diagnosis and treatment planning.
Existing methods often struggle with precise delineation of complex tumor structures like peritumoral edema and necrotic core.

Purpose of the Study:

To develop and evaluate a novel deep learning model, the Multi-Planar Spatial Convolutional Neural Network (MPS-CNN), for automated brain tumor segmentation.
To improve the accuracy of segmenting various tumor sub-regions, including peritumoral edema (ED), necrotic core (NCR), and enhancing (ET) and non-enhancing tumor core (NET).

Main Methods:

An encoder-decoder CNN architecture was designed for pixel-wise segmentation across axial, sagittal, and coronal planes.
A consensus fusion strategy combined multi-planar segmentations, refined by a Conditional Random Field (CRF) for post-processing.
Spatial-pooling and unpooling layers were incorporated to preserve spatial information and reduce boundary segmentation errors.
An aggregated loss function was developed to address data imbalance issues.

Main Results:

The MPS-CNN achieved high Dice scores on the BraTS 2018 validation dataset: 0.90216 for whole tumor (WT), 0.87247 for tumor core (TC), and 0.82445 for enhancing tumor (ET).
The model demonstrated top performance for ET and TC segmentation, excelling in both Dice and Hausdorff distance metrics.
For WT segmentation, MPS-CNN achieved the second-highest accuracy, closely matching the best-performing method.

Conclusions:

The proposed MPS-CNN model offers effective and automated segmentation of brain tumor sub-regions from multimodal MRI.
Its multi-planar approach and CRF-based refinement contribute to high accuracy in delineating tumor boundaries and components.
MPS-CNN shows significant potential for clinical application in neuro-oncology, outperforming existing methods in key segmentation tasks.