# Deep Geodesic Learning for Segmentation and Anatomical Landmarking

arXiv: https://arxiv.org/abs/1810.04021 · HTML: https://mlsanity.com/paper/1810.04021/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2018.

## Built on by (most cited first, 8 of 8)

- [Uncertainty Estimation for Heatmap-based Landmark Localization](https://mlsanity.com/paper/2203.02351/) (2022)
- [Joint Landmark and Structure Learning for Automatic Evaluation of Developmental Dysplasia of the Hip](https://mlsanity.com/paper/2106.05458/) (2021)
- [SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection](https://mlsanity.com/paper/2110.03828/) (2021)
- [Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection](https://mlsanity.com/paper/2107.09899/) (2021)
- [CABLD: Contrast-Agnostic Brain Landmark Detection with Consistency-Based Regularization](https://mlsanity.com/paper/2411.17845/) (2024)
- [Convolutional Neural Networks in Orthodontics: a review](https://mlsanity.com/paper/2104.08886/) (2021)
- [AI Techniques for Cone Beam Computed Tomography in Dentistry: Trends and Practices](https://mlsanity.com/paper/2306.03025/) (2023)
- [Surface-to-Skeleton 3D Cephalometry: Estimating Hidden Skeletal Landmarks from CT-Derived External Soft-Tissue Surfaces](https://mlsanity.com/paper/2608.12537/) (2026)

## Abstract (arXiv)

In this paper, we propose a novel deep learning framework for anatomy segmentation and automatic landmark- ing. Specifically, we focus on the challenging problem of mandible segmentation from cone-beam computed tomography (CBCT) scans and identification of 9 anatomical landmarks of the mandible on the geodesic space. The overall approach employs three inter-related steps. In step 1, we propose a deep neu- ral network architecture with carefully designed regularization, and network hyper-parameters to perform image segmentation without the need for data augmentation and complex post- processing refinement. In step 2, we formulate the landmark localization problem directly on the geodesic space for sparsely- spaced anatomical landmarks. In step 3, we propose to use a long short-term memory (LSTM) network to identify closely- spaced landmarks, which is rather difficult to obtain using other standard detection networks. The proposed fully automated method showed superior efficacy compared to the state-of-the- art mandible segmentation and landmarking approaches in craniofacial anomalies and diseased states. We used a very challenging CBCT dataset of 50 patients with a high-degree of craniomaxillofacial (CMF) variability that is realistic in clinical practice. Complementary to the quantitative analysis, the qualitative visual inspection was conducted for distinct CBCT scans from 250 patients with high anatomical variability. We have also shown feasibility of the proposed work in an independent dataset from MICCAI Head-Neck Challenge (2015) achieving the state-of-the-art performance. Lastly, we present an in-depth analysis of the proposed deep networks with respect to the choice of hyper-parameters such as pooling and activation functions.