# Deep Active Lesion Segmentation

arXiv: https://arxiv.org/abs/1908.06933 · HTML: https://mlsanity.com/paper/1908.06933/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

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

- [A survey on shape-constraint deep learning for medical image segmentation](https://mlsanity.com/paper/2101.07721/) (2021)
- [AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge](https://mlsanity.com/paper/2310.04677/) (2023)
- [XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysis](https://mlsanity.com/paper/2306.03983/) (2023)
- [Chan-Vese Attention U-Net: An attention mechanism for robust segmentation](https://mlsanity.com/paper/2306.16098/) (2023)

## Abstract (arXiv)

Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors. We introduce Deep Active Lesion Segmentation (DALS), a fully automated segmentation framework for that leverages the powerful nonlinear feature extraction abilities of fully Convolutional Neural Networks (CNNs) and the precise boundary delineation abilities of Active Contour Models (ACMs). Our DALS framework benefits from an improved level-set ACM formulation with a per-pixel-parameterized energy functional and a novel multiscale encoder-decoder CNN that learns an initialization probability map along with parameter maps for the ACM. We evaluate our lesion segmentation model on a new Multiorgan Lesion Segmentation (MLS) dataset that contains images of various organs, including brain, liver, and lung, across different imaging modalities---MR and CT. Our results demonstrate favorable performance compared to competing methods, especially for small training datasets. Source code : https://github.com/ahatamiz/dals