# Two Deep Learning Approaches for Automated Segmentation of Left Ventricle in Cine Cardiac MRI

arXiv: https://arxiv.org/abs/2601.00794 · HTML: https://mlsanity.com/paper/2601.00794/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 2 Jan 2026.

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

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Batch-Instance Normalization for Adaptively Style-Invariant Neural Networks](https://mlsanity.com/paper/1805.07925/) (2018)
- [A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI](https://mlsanity.com/paper/1604.00494/) (2016)
- [Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation](https://mlsanity.com/paper/1608.03974/) (2016)
- [Normalization in Training U-Net for 2D Biomedical Semantic Segmentation](https://mlsanity.com/paper/1809.03783/) (2018)
- [U-Net Training with Instance-Layer Normalization](https://mlsanity.com/paper/1908.08466/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Novel Deep Learning Method for Segmenting the Left Ventricle in Cardiac Cine MRI](https://mlsanity.com/paper/2601.01512/) (2026)

## Abstract (arXiv)

Left ventricle (LV) segmentation is critical for clinical quantification and diagnosis of cardiac images. In this work, we propose two novel deep learning architectures called LNU-Net and IBU-Net for left ventricle segmentation from short-axis cine MRI images. LNU-Net is derived from layer normalization (LN) U-Net architecture, while IBU-Net is derived from the instance-batch normalized (IB) U-Net for medical image segmentation. The architectures of LNU-Net and IBU-Net have a down-sampling path for feature extraction and an up-sampling path for precise localization. We use the original U-Net as the basic segmentation approach and compared it with our proposed architectures. Both LNU-Net and IBU-Net have left ventricle segmentation methods: LNU-Net applies layer normalization in each convolutional block, while IBU-Net incorporates instance and batch normalization together in the first convolutional block and passes its result to the next layer. Our method incorporates affine transformations and elastic deformations for image data processing. Our dataset that contains 805 MRI images regarding the left ventricle from 45 patients is used for evaluation. We experimentally evaluate the results of the proposed approaches outperforming the dice coefficient and the average perpendicular distance than other state-of-the-art approaches.