Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans

Abstract

In this paper, we investigate the automatic detection of white and brown adipose tissues using Positron Emission Tomography/Computed Tomography (PET/CT) scans, and develop methods for the quantification of these tissues at the whole-body and body-region levels. We propose a patient-specific automatic adiposity analysis system with two modules. In the first module, we detect white adipose tissue (WAT) and its two sub-types from CT scans: Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT). This process relies conventionally on manual or semi-automated segmentation, leading to inefficient solutions. Our novel framework addresses this challenge by proposing an unsupervised learning method to separate VAT from SAT in the abdominal region for the clinical quantification of central obesity. This step is followed by a context driven label fusion algorithm through sparse 3D Conditional Random Fields (CRF) for volumetric adiposity analysis. In the second module, we automatically detect, segment, and quantify brown adipose tissue (BAT) using PET scans because unlike WAT, BAT is metabolically active. After identifying BAT regions using PET, we perform a co-segmentation procedure utilizing asymmetric complementary information from PET and CT. Finally, we present a new probabilistic distance metric for differentiating BAT from non-BAT regions. Both modules are integrated via an automatic body-region detection unit based on one-shot learning. Experimental evaluations conducted on 151 PET/CT scans achieve state-of-the-art performances in both central obesity as well as brown adiposity quantification.

Fig. 1.

An illustration of different types of adipose tissues in Positron Emission Tomography (PET) and Computed Tomography (CT) scans. (A) signifies the difference at cellular level between Brown Adipose Tissue (BAT) and White Adipose Tissue (WAT). In contrast to WAT, BAT is metabolically active and consumes energy. (B) shows Subcutaneous Adipose Tissue (SAT) and Visceral Adipose Tissue (VAT) in a coronal view of CT. The red boundary illustrates the thin muscular wall separating these two sub-types. The wall remains mostly discontinuous, making SAT-VAT separation significantly challenging. (C) depicts metabolically active BAT in PET (left/middle) and PET/CT fusion (right).

Fig. 2.

A flow diagram of the proposed system for whole-body adiposity analysis. The input to the system comprises PET/CT images. Thorax and abdominal regions are detected using deep learning features in the first stage (Section III), followed by Subcutaneous-Visceral adipose tissue segmentation (Section IV) using CT images, and Brown Adipose Tissue detection and quantification (Section V) using PET images.

Fig. 3.

An overview of the proposed SAT-VAT separation method. Once the abdominal region is detected, Total Adipose Tissue (TAT) is segmented using CT intensity interval known for fat tissue. Initial Subcutaneous-Visceral adipose tissue boundary is estimated by evaluating multiple hypothesis points. Geometric Median Absolute Deviation (MAD) and appearance based Local Outlier Scores (LoOS) are then combined within the 3D Conditional Random Field (CRF) based label fusion.

Fig. 4.

An illustration of skin boundary and hypothesis points along the radii connecting S with its centroid C. For each point (yellow) in S, a set of hypotheses (blue) is generated which is along the line connecting the skin boundary point with the centroid C.

Fig. 5.

An overview of the proposed Brown Adipose Tissue (BAT) detection and segmentation system. Given the head-neck and thorax regions, adipose tissue is identified using CT thresholding intervals (Step 1). Using the corresponding PET scans, segmentation seeds are sampled in accordance with high uptake regions (Step 2). PET-CT co-segmentation is performed using Random Walk (Step 3) followed by false positive removal (Step 4) using Total Variation and Cramér-von Mises distances.

Fig. 6.

Visceral Adipose Tissue (red) and Subcutaneous Adipose Tissue (green) segmentations are illustrated for two subjects (one with BMI<25, another with BMI>30) at the chosen abdominal slice level along with their volume renderings. Several abdominal slices are also shown for central adiposity accumulation.

Fig. 7.

For three different anatomical levels (columns), row (A) shows reference standards (white); row (B) demonstrates the results from CT thresholding where pink (inner) and blue (outer) contours show brown fat delineation (blue contour shows fat region near skin boundary which leaks into the body cavity and also overlaps with pink contour as in the first column); row (C) comprises the results from ROI (Region of Interest) based CT thresholding, where orange boxes show user drawn ROIs and blue contours are the brown fat delineation results; row (D) shows the results from conventional PET thresholding, where green contours show output BAT delineations; row (E) depicts the ROI based PET thresholding; and row (F) demonstrates the proposed algorithm’s delineation results using PET and CT jointly. (G) Dice Similarity Coefficients (DSC) of the proposed method in comparison with ROI based PET thresholding, PET thresholding, ROI based CT thresholding, and CT thresholding methods are shown.