Significance: Re-excision rates for women with invasive breast cancer undergoing breast conserving surgery (or lumpectomy) have decreased in the past decade but remain substantial. This is mainly due to the inability to assess the entire surface of an excised lumpectomy specimen efficiently and accurately during surgery.
Aim: The goal of this study was to develop a deep-ultraviolet scanning fluorescence microscope (DUV-FSM) that can be used to accurately and rapidly detect cancer cells on the surface of excised breast tissue.
Approach: A DUV-FSM was used to image the surfaces of 47 (31 malignant and 16 normal/benign) fresh breast tissue samples stained in propidium iodide and eosin Y solutions. A set of fluorescence images were obtained from each sample using low magnification (4 × ) and fully automated scanning. The images were stitched to form a color image. Three nonmedical evaluators were trained to interpret and assess the fluorescence images. Nuclear–cytoplasm ratio (N/C) was calculated and used for tissue classification.
Results: DUV-FSM images a breast sample with subcellular resolution at a speed of 1.0 min / cm2. Fluorescence images show excellent visual contrast in color, tissue texture, cell density, and shape between invasive carcinomas and their normal counterparts. Visual interpretation of fluorescence images by nonmedical evaluators was able to distinguish invasive carcinoma from normal samples with high sensitivity (97.62%) and specificity (92.86%). Using N/C alone was able to differentiate patch-level invasive carcinoma from normal breast tissues with reasonable sensitivity (81.5%) and specificity (78.5%).
Conclusions: DUV-FSM achieved a good balance between imaging speed and spatial resolution with excellent contrast, which allows either visual or quantitative detection of invasive cancer cells on the surfaces of a breast surgical specimen.
Breast cancer is the most commonly diagnosed cancer among women. Positive margin status after breast-conserving surgery (BCS) is a predictor of higher rates of local recurrence. Intraoperative margin detection helps to complete tumor excision at the first operation. A margin tool that is capable of imaging all six margins of large lumpectomy specimens with both high resolution and fast speed (within 20 min) is yet to be developed. Deep UV light allows simultaneous excitation of multiple fluorophores and generating surface fluorescence images. We have developed a deep UV fluorescence scanning microscope (DUV-FSM) for slide-free, high-resolution and rapid examination of tumor specimens during BCS. The DUV-FSM uses a deep UV LED for oblique back illumination of freshly excised breast tissues stained with propidium iodide and Eosin Y and motorized XY stages for mosaic scanning. Fluorescence images are captured by a color CCD camera. Both invasive lobular carcinoma (ILC) and invasive ductal carcinoma (IDC) images showed excellent contrast from that of the normal cells in color, tissue texture, and cell density and shapes. This contrast have been consistently observed in all samples (n = 20) we have imaged so far. Statistical analysis showed a significant difference (p<0.0001) in nucleus-to-cytoplasm (NC) ratio between normal and invasive tissues. Thus, it may be utilized either visually by a trained individual or quantitatively by an algorithm to detect positive margins of lumpectomy specimens intraoperatively.
Model based iterative reconstruction (MBIR) algorithms have shown significant improvement in CT image
quality by increasing resolution as well as reducing noise and artifacts. In diagnostic protocols, radiologists
often need the high-resolution reconstruction of a limited region of interest (ROI). This ROI reconstruction
is complicated for MBIR which should reconstruct an image in a full field of view (FOV) given full sinogram
measurements. Multi-resolution approaches are widely used for this ROI reconstruction of MBIR, in which the
image with a full FOV is reconstructed in a low-resolution and the forward projection of non-ROI is subtracted
from the original sinogram measurements for high-resolution ROI reconstruction. However, a low-resolution
reconstruction of a full FOV can be susceptible to streaking and blurring artifacts and these can be propagated
into the following high-resolution ROI reconstruction. To tackle this challenge, we use a coupled dictionary
representation model between low- and high-resolution training dataset for artifact removal and super resolution
of a low-resolution full FOV reconstruction. Experimental results on phantom data show that the restored full
FOV reconstruction via a coupled dictionary learning significantly improve the image quality of high-resolution
ROI reconstruction for MBIR.
This paper describes a fast multi-scale vessel enhancement filter in 3D medical images. For efficient review
of the vascular information, clinicians need rendering the 3D vascular information as a 2D image. Generally,
the maximum intensity projection (MIP) is a useful and widely used technique for producing a 2D image from
the 3D vascular data. However, the MIP algorithm reduces the conspicuousness for small and faint vessels
owing to the overlap of non-vascular structures. To overcome this invisibility, researchers have examined the
multi-scale vessel enhancement filter based on a combination of the eigenvalues of the 3D Hessian matrix. This
multi-scale vessel enhancement filter produces higher contrast. However, it is time-consuming and requires high
cost computation due to large volume of data and complex 3D convolution. For fast vessel enhancement, we
propose a novel multi-scale vessel enhancement filter using 3D integral images and 3D approximated Gaussian
kernel. This approximated kernel looks like cube but it is not exact cube. Each layer of kernel is approximated
2D Gaussian second order derivative by dividing it into three rectangular regions whose sum is integer. 3D
approximated kernel is a pile of these 2D box kernels which are normalized by Frobenius norm. Its size fits to
vessel width in order to achieve better visualization of the small vessel. Proposed method is approximately five
times faster and produces comparable results with previous multi-scale vessel enhancement filter.