# A Systematic Approach for MRI Brain Tumor Localization, and Segmentation using Deep Learning and Active Contouring

arXiv: https://arxiv.org/abs/2102.03532 · HTML: https://mlsanity.com/paper/2102.03532/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 6 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [Deep Learning with Mixed Supervision for Brain Tumor Segmentation](https://mlsanity.com/paper/1812.04571/) (2018)
- [Segmentation of Glioma Tumors in Brain Using Deep Convolutional Neural Network](https://mlsanity.com/paper/1708.00377/) (2017)
- [Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images](https://mlsanity.com/paper/1809.07786/) (2018)
- [Brain Tumor Classification Using Deep Learning Technique -- A Comparison between Cropped, Uncropped, and Segmented Lesion Images with Different Sizes](https://mlsanity.com/paper/2001.08844/) (2020)
- [Brain Tumor Segmentation by Cascaded Deep Neural Networks Using Multiple Image Scales](https://mlsanity.com/paper/2002.01975/) (2020)

## Built on by (most cited first, 4 of 4)

- [Brain Tumor Segmentation from MRI Images using Deep Learning Techniques](https://mlsanity.com/paper/2305.00257/) (2023)
- [Fuzzy Information Seeded Region Growing for Automated Lesions After Stroke Segmentation in MR Brain Images](https://mlsanity.com/paper/2311.11742/) (2023)
- [Brain Tumor Classification by Cascaded Multiscale Multitask Learning Framework Based on Feature Aggregation](https://mlsanity.com/paper/2112.14320/) (2021)
- [Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection](https://mlsanity.com/paper/2507.00903/) (2025)

## Abstract (arXiv)

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First classifiers are implemented with a deep convolutional neural network(CNN) andsecond a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentratedtumor boundary is contoured for the segmentation process by using the Chan-Vesesegmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, Chan- Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score,Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean absolute error (MAE), and Peak Signal to Noise Ratio (PSNR) werecalculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with average dice score of 0.92, (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076 and MAE of 52.946), pointing to high reliability of the proposed architecture.