# Mask Mining for Improved Liver Lesion Segmentation

## TL;DR

This paper introduces a novel error-aware training method for U-Net models that enhances liver and lesion segmentation accuracy in CT scans by focusing on reducing false positives and improving recall, demonstrated on LiTS data.

## Contribution

The proposed method incorporates segmentation errors into the training process, enabling models to learn features that mitigate previous mistakes, which is a novel approach in liver lesion segmentation.

## Key findings

- Up to 2-point increase in dice score on LiTS dataset
- Effective across multiple U-Net architectures
- Improves recall and reduces false positives

## Abstract

We propose a novel procedure to improve liver and lesion segmentation from CT scans for U-Net based models. Our method extends standard segmentation pipelines to focus on higher target recall or reduction of noisy false-positive predictions, boosting overall segmentation performance. To achieve this, we include segmentation errors into a new learning process appended to the main training setup, allowing the model to find features which explain away previous errors. We evaluate this on semantically distinct architectures: cascaded two- and three-dimensional as well as combined learning setups for multitask segmentation. Liver and lesion segmentation data are provided by the Liver Tumor Segmentation challenge (LiTS), with an increase in dice score of up to 2 points.