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Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks (1702.05970v2)

Published 20 Feb 2017 in cs.CV and cs.AI

Abstract: Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of a large-scale medical trial or quantitative image analysis. We train and cascade two FCNs for a combined segmentation of the liver and its lesions. In the first step, we train a FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions within the predicted liver ROIs of step 1. CFCN models were trained on an abdominal CT dataset comprising 100 hepatic tumor volumes. Validations on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for liver with computation times below 100s per volume. We further experimentally demonstrate the robustness of the proposed method on an 38 MRI liver tumor volumes and the public 3DIRCAD dataset.

Citations (316)

Summary

  • The paper introduces a two-step cascaded FCN approach that first segments the liver and then isolates lesions for improved precision.
  • It achieves Dice similarity scores exceeding 94% for liver and up to 69.7% for lesions across varied CT and MRI datasets.
  • The study underscores the method's potential to reduce manual diagnostic workload while enhancing repeatable, accurate medical imaging analysis.

An Insightful Analysis of Automatic Liver and Tumor Segmentation Using Cascaded FCNs

The paper presents a substantial effort in advancing medical image analysis, specifically focusing on the automated segmentation of liver and hepatic lesions within computed tomography (CT) and magnetic resonance imaging (MRI) volumes. The authors propose a novel approach involving the use of cascaded fully convolutional neural networks (CFCNs) to achieve accurate semantic segmentation, which is critical for clinical diagnosis and the development of computer-aided decision support systems.

Methodology Overview

The crux of the paper is the implementation of a cascaded architecture of fully convolutional neural networks (FCNs). This methodology involves a two-step process:

  1. Initial Liver Segmentation: The prime objective is to define the liver as a region of interest (ROI). An FCN is trained to segment the liver from abdominal CT or MRI slices. This step is crucial as it isolates the liver, allowing subsequent processes to concentrate specifically on this organ.
  2. Lesion Segmentation Within Liver ROI: In this second phase, a subsequent FCN focuses exclusively on the areas within the liver’s ROI from the first step, segmenting hepatic lesions with higher precision.

Enhanced by preprocessing techniques (e.g., image windowing and augmentation) and the application of dense 3D Conditional Random Fields (CRFs) for post-processing, the cascaded approach effectively improves segmentation quality by integrating both spatial and semantic features of the input images.

Numerical Results and Claims

The research demonstrates robust results across multiple datasets, including the public 3DIRCAD dataset and an additional private clinical dataset. The model achieves noteworthy performances with Dice similarity coefficients exceeding 94% for liver segmentation and up to 69.7% for lesion segmentation on different datasets. These results underscore the effectiveness of the CFCN in addressing the challenges of low contrast, variable liver lesions.

A distinctive claim within the paper is the method's ability to generalize across modalities—an assertion supported through applied experimentation on CT and MRI datasets. This generalization across varying image acquisition protocols indicates the promise of CFCNs to uniformly address different imaging challenges prevalent in clinical settings.

Implications and Future Directions

Practically, the adoption of CFCNs for automatic liver and lesion segmentation can revolutionize radiology workflows by significantly reducing human labor while maintaining high precision and repeatability. The inherent efficiency facilitates large-scale clinical trials and could potentially expedite routine diagnostic processes in hospitals.

Theoretically, despite the robust performance demonstrated by the cascaded approach, integrating 3D Convolutional Neural Networks (CNNs) or employing transfer learning strategies could enhance model robustness and accuracy further. Additionally, automatic tuning of CRF parameters could be explored to optimize performance on heterogeneous and low-contrast cases.

The authors deftly handle the complex task of liver and lesion segmentation, setting a foundation for future work in combining imaging modalities and advancing interpretations of heterogeneous datasets. These findings open the avenue for future work enhancing segmentation accuracy, exploring state-of-the-art data augmentation techniques, and investigating potential applications of segmented features in diagnosis and prognosis models.

In conclusion, this paper contributes effectively to the field of medical imaging by demonstrating the utility of CFCNs in real-life clinical scenarios. The implications are far-reaching, offering a pathway to enhance existing diagnostic tools and processes in the medical imaging landscape.