# Spine Landmark Localization with combining of Heatmap Regression and Direct Coordinate Regression

arXiv: https://arxiv.org/abs/2007.05355 · HTML: https://mlsanity.com/paper/2007.05355/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Multi-Scale Context-Guided Lumbar Spine Disease Identification with Coarse-to-fine Localization and Classification](https://mlsanity.com/paper/2203.08408/) (2022)
- [Localizing Anatomical Landmarks in Ocular Images using Zoom-In Attentive Networks](https://mlsanity.com/paper/2210.02445/) (2022)

## Abstract (arXiv)

Landmark Localization plays a very important role in processing medical images as well as in disease identification. However, In medical field, it's a challenging task because of the complexity of medical images and the high requirement of accuracy for disease identification and treatment.There are two dominant ways to regress landmark coordination, one using the full convolutional network to regress the heatmaps of landmarks , which is a complex way and heatmap post-process strategies are needed, and the other way is to regress the coordination using CNN + Full Connective Network directly, which is very simple and faster training , but larger dataset and deeper model are needed to achieve higher accuracy. Though with data augmentation and deeper network it can reach a reasonable accuracy, but the accuracy still not reach the requirement of medical field. In addition, a deeper networks also means larger space consumption. To achieve a higher accuracy, we contrived a new landmark regression method which combing heatmap regression and direct coordinate regression base on probability methods and system control theory.