Improving Segmentation and Detection of Lesions in CT Scans Using Intensity Distribution Supervision

TL;DR

This paper introduces a novel intensity distribution supervision method for CT scan lesion segmentation and detection, leveraging an intensity-based lesion probability map to enhance network training without extra labeling, significantly improving performance across multiple lesion types.

Contribution

The study presents a new approach that incorporates intensity information as supervision in training segmentation and detection networks, improving accuracy without additional labeling costs.

Findings

Segmentation Dice scores improved for all lesion types.

Detection average precision increased notably for kidney tumors.

Method effectively utilizes intensity histograms for lesion localization.

Abstract

We propose a method to incorporate the intensity information of a target lesion on CT scans in training segmentation and detection networks. We first build an intensity-based lesion probability (ILP) function from an intensity histogram of the target lesion. It is used to compute the probability of being the lesion for each voxel based on its intensity. Finally, the computed ILP map of each input CT scan is provided as additional supervision for network training, which aims to inform the network about possible lesion locations in terms of intensity values at no additional labeling cost. The method was applied to improve the segmentation of three different lesion types, namely, small bowel carcinoid tumor, kidney tumor, and lung nodule. The effectiveness of the proposed method on a detection task was also investigated. We observed improvements of 41.3% -> 47.8%, 74.2% -> 76.0%, and 26.4% -> 32.7% in segmenting small bowel carcinoid tumor, kidney tumor, and lung nodule, respectively, in terms of per case Dice scores. An improvement of 64.6% -> 75.5% was achieved in detecting kidney tumors in terms of average precision. The results of different usages of the ILP map and the effect of varied amount of training data are also presented.