Data release for "Symbolic and Hybrid AI for Brain Tissue Segmentation using Spatial Model Checking"

This repository contains the data of the article "Symbolic and Hybrid AI for Brain Tissue Segmentation using Spatial Model Checking" by Gina Belmonte, Vincenzo Ciancia, and Mieke Massink, accepted for publication at the international scientific journal "AI in Medicine", 2025, publisher Elsevier.

Abstract:

Segmentation of 3D medical images, and brain segmentation in particular, is an important topic in neuroimaging and in radiotherapy. Overcoming the current, time consuming, practise of manual delineation of brain tumours and providing an accurate, explainable, and replicable method of segmentation of the tumour area and related tissues is therefore an open research challenge. In this paper, we first propose a novel symbolic approach to brain segmentation and delineation of brain lesions based on spatial model checking. This method has its foundations in the theory of closure spaces, a generalisation of topological spaces, and spatial logics. At its core is a high-level declarative logic language for image analysis, ImgQL, and an efficient spatial model checker, VoxLogicA, exploiting state-of-the-art image analysis libraries in its model checking algorithm. We then illustrate how this technique can be combined with Machine Learning techniques leading to a hybrid AI approach that provides accurate and explainable segmentation results. We show the results of the application of the symbolic approach on several public datasets with 3D magnetic resonance (MR) images. Three datasets are provided by the 2017, 2019 and 2020 international MICCAI BraTS Challenges with 210, 259 and 293 MR images, respectively, and the fourth is the BrainWeb dataset with 20 (synthetic) 3D patient images of the normal brain. We then apply the hybrid AI method to the BraTS 2020 training set. Our segmentation results are shown to be in line with the state-of-the-art with respect to other recent approaches, both from the accuracy point of view as well as from the view of computational efficiency, but with the advantage of them being explainable.

Description of data

Data for BraTS 2020 (Table 4 of the article):

full_stats_for_fixed_thresholds.csv

Data for BrainWeb case study (Table 5 of the article):

brainweb-grey-white.csv

Data for the hybrid case study (Table 6 of the article):

Results:

• full_stats_for_found_thresholds.csv
• full_stats_for_optimal_thresholds.csv
• nnUnet_results_2025.csv

Auxiliary files:

Name mapping BraTS 2020 dataset:

• BRATS2020_name_mapping.csv

Files that were discarded in the referenced TACAS19 publication:

• discarded_tacas19.csv

Files used in TACAS19 to manually create the logical specification:

• manual_calibration_IMGQL_SPEC-BraTS17.csv

Fifty images used for training nnUNet:

• 50images-nnUNet-Training.csv