# Systematic Review of Pituitary Gland and Pituitary Adenoma Automatic Segmentation Techniques in Magnetic Resonance Imaging

arXiv: https://arxiv.org/abs/2506.19797 · HTML: https://mlsanity.com/paper/2506.19797/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 144 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 11 of 11)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Medical Image Segmentation Review: The success of U-Net](https://mlsanity.com/paper/2211.14830/) (2022)
- [Brain Tumor Segmentation with Deep Neural Networks](https://mlsanity.com/paper/1505.03540/) (2015)
- [Optimizing the Dice Score and Jaccard Index for Medical Image Segmentation: Theory & Practice](https://mlsanity.com/paper/1911.01685/) (2019)
- [QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy](https://mlsanity.com/paper/1801.04161/) (2018)
- [A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network](https://mlsanity.com/paper/2402.05975/) (2024)
- [MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification](https://mlsanity.com/paper/2107.12321/) (2021)
- [Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery](https://mlsanity.com/paper/2007.11784/) (2020)
- [QuickTumorNet: Fast Automatic Multi-Class Segmentation of Brain Tumors](https://mlsanity.com/paper/2012.12410/) (2020)
- [Brain Tumor Segmentation from MRI Images using Deep Learning Techniques](https://mlsanity.com/paper/2305.00257/) (2023)
- [Preoperative Volume Determination for Pituitary Adenoma](https://mlsanity.com/paper/1602.02022/) (2016)

## Abstract (arXiv)

Purpose: Accurate segmentation of both the pituitary gland and adenomas from magnetic resonance imaging (MRI) is essential for diagnosis and treatment of pituitary adenomas. This systematic review evaluates automatic segmentation methods for improving the accuracy and efficiency of MRI-based segmentation of pituitary adenomas and the gland itself. Methods: We reviewed 34 studies that employed automatic and semi-automatic segmentation methods. We extracted and synthesized data on segmentation techniques and performance metrics (such as Dice overlap scores). Results: The majority of reviewed studies utilized deep learning approaches, with U-Net-based models being the most prevalent. Automatic methods yielded Dice scores of 0.19--89.00% for pituitary gland and 4.60--96.41% for adenoma segmentation. Semi-automatic methods reported 80.00--92.10% for pituitary gland and 75.90--88.36% for adenoma segmentation. Conclusion: Most studies did not report important metrics such as MR field strength, age and adenoma size. Automated segmentation techniques such as U-Net-based models show promise, especially for adenoma segmentation, but further improvements are needed to achieve consistently good performance in small structures like the normal pituitary gland. Continued innovation and larger, diverse datasets are likely critical to enhancing clinical applicability.