2,061 research outputs found
Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions
Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio
A Cascaded Approach for ultraly High Performance Lesion Detection and False Positive Removal in Liver CT Scans
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)
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