A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans

TL;DR

This paper presents a two-stage cascaded approach for liver lesion detection in multi-phase CT scans, achieving high sensitivity and specificity in malignancy classification and false positive removal.

Contribution

It introduces a large-scale, multi-phase CT dataset and a novel two-stage detection pipeline combining high-sensitivity detection with false positive reduction.

Findings

Achieved 99.2% sensitivity and 97.1% specificity in malignancy diagnosis.

Achieved 97.3% sensitivity and 95.7% specificity in screening.

Developed a multi-object labeling tool and a lesion-shuffle augmentation method.

Abstract

Liver cancer has high morbidity and mortality rates in the world. Multi-phase CT is a main medical imaging modality for detecting/identifying and diagnosing liver tumors. Automatically detecting and classifying liver lesions in CT images have the potential to improve the clinical workflow. This task remains challenging due to liver lesions' large variations in size, appearance, image contrast, and the complexities of tumor types or subtypes. In this work, we customize a multi-object labeling tool for multi-phase CT images, which is used to curate a large-scale dataset containing 1,631 patients with four-phase CT images, multi-organ masks, and multi-lesion (six major types of liver lesions confirmed by pathology) masks. We develop a two-stage liver lesion detection pipeline, where the high-sensitivity detecting algorithms in the first stage discover as many lesion proposals as possible, and the lesion-reclassification algorithms in the second stage remove as many false alarms as possible. The multi-sensitivity lesion detection algorithm maximizes the information utilization of the individual probability maps of segmentation, and the lesion-shuffle augmentation effectively explores the texture contrast between lesions and the liver. Independently tested on 331 patient cases, the proposed model achieves high sensitivity and specificity for malignancy classification in the multi-phase contrast-enhanced CT (99.2%, 97.1%, diagnosis setting) and in the noncontrast CT (97.3%, 95.7%, screening setting).