Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

Unger, Jakob; Sun, Tianchen; Phipps, Jennifer E.; Bold, Richard J.; Darrow, Morgan A.; Ma, Kwan‐Liu; Marcu, Laura

doi:10.1117/1.jbo.23.1.015001

Cited by 17 publications

(26 citation statements)

References 32 publications

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“…For each patient, one piece of tissue with a diameter of 15 to 30 mm that was assumed to contain a tumor was studied. Prior to imaging, a 405nm continuous-wave (CW) laser diode was used to generate 4 to 8 clearly observable landmarks (fiducial points) on the tissue block in order to enable an accurate registration between the video images and the corresponding histology slides [23]. Histology confirmed 14 specimens with the diagnosis of invasive cancer and 4 specimens with DCIS.…”

Section: Breast Specimensmentioning

confidence: 99%

“…Histology confirmed 14 specimens with the diagnosis of invasive cancer and 4 specimens with DCIS. Note that the specimens used for a recent image registration study [23] have been also used in this study.…”

Section: Breast Specimensmentioning

confidence: 99%

“…1(c)) for mobility on demand. Matlab (Mathworks, Inc.) was used for registering autofluorescence imaging data and cross-sectional histology slides [23]. A hand-guided scan was performed for each specimen.…”

Section: Instrumentation and Imaging Setupmentioning

confidence: 99%

“…In addition, (3) we show an iterative imaging refinement that allows a surgeon to switch between rapid scans with a low spatial resolution and slower scans with enhanced resolution in suspicious regions where more details are required. The supervised learning scheme and its evaluation were based on a well characterized set of samples (N=18 patients) leveraging on a previously developed registration technique between autofluorescence imaging data and cross-sectional histology slides [23].…”

Section: Introductionmentioning

confidence: 99%

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Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning

Unger

Hebisch

Phipps

et al. 2020

Biomed. Opt. Express

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Tumor-free surgical margins are critical in breast-conserving surgery. In up to 38% of the cases, however, patients undergo a second surgery since malignant cells are found at the margins of the excised resection specimen. Thus, advanced imaging tools are needed to ensure clear margins at the time of surgery. The objective of this study was to evaluate a random forest classifier that makes use of parameters derived from point-scanning label-free fluorescence lifetime imaging (FLIm) measurements of breast specimens as a means to diagnose tumor at the resection margins and to enable an intuitive visualization of a probabilistic classifier on tissue specimen. FLIm data from fresh lumpectomy and mastectomy specimens from 18 patients were used in this study. The supervised training was based on a previously developed registration technique between autofluorescence imaging data and cross-sectional histology slides. A pathologist's histology annotations provide the ground truth to distinguish between adipose, fibrous, and tumor tissue. Current results demonstrate the ability of this approach to classify the tumor with 89% sensitivity and 93% specificity and to rapidly (∼ 20 frames per second) overlay the probabilistic classifier overlaid on excised breast specimens using an intuitive color scheme. Furthermore, we show an iterative imaging refinement that allows surgeons to switch between rapid scans with a customized, low spatial resolution to quickly cover the specimen and slower scans with enhanced resolution (400 µm per point measurement) in suspicious regions where more details are required. In summary, this technique provides high diagnostic prediction accuracy, rapid acquisition, adaptive resolution, nondestructive probing, and facile interpretation of images, thus holding potential for clinical breast imaging based on label-free FLIm.

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Section: Breast Specimensmentioning

confidence: 99%

Section: Breast Specimensmentioning

confidence: 99%

Section: Instrumentation and Imaging Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning

Unger

Hebisch

Phipps

et al. 2020

Biomed. Opt. Express

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“…The calibration techniques can be realized in real time and implemented using mmSFE for standardized in vivo fluorescence imaging, along with preprocedure and possibly postprocedure calibrations. The multistep registrations can be automated using laser-induced fiducial landmarks 40 and algorithm-selected landmarks. 41 When BLZ-100 NIR fluorescence is compared to three other fluorescence agents in Elliott et al, 26 in general, their performance in detecting glioma tumor area is comparable.…”

Section: Sensitivitymentioning

confidence: 99%

Calibration of fluorescence imaging for tumor surgical margin delineation: multistep registration of fluorescence and histological images

et al. 2019

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Calibration of fluorescence imaging for tumor surgical margin delineation: multistep registration of fluorescence and histological images,"Abstract. Although a greater extent of tumor resection is important for patients' survival, complete tumor removal, especially tumor margins, remains challenging due to the lack of sensitivity and specificity of current surgical guidance techniques at the margins. Intraoperative fluorescence imaging with targeted fluorophores is promising for tumor margin delineation. To verify the tumor margins detected by the fluorescence images, it is necessary to register fluorescence with histological images, which provide the ground truth for tumor regions. However, current registration methods compare fluorescence images to a single-layer histological slide, which is selected subjectively and represents a single plane of the three-dimensional tumor. A multistep pipeline is established to correlate fluorescence images to stacked histological images, including fluorescence calibration and multistep registration. Multiple histological slices are integrated as a two-dimensional (2-D) tumor map using optical attenuation model and average intensity projection. A BLZ-100-labeled medulloblastoma mouse model is used to test the whole framework. On average, the synthesized 2-D tumor map outperforms the selected best slide as ground truth [Dice similarity coefficient (DSC): 0.582 versus 0.398, with significant differences; mean area under the curve (AUC) of the receiver operating characteristic curve: 88% versus 85.5%] and the randomly selected slide as ground truth (DSC: 0.582 versus 0.396 with significant differences; mean AUC: 88% versus 84.1% with significant differences), which indicates our pipeline is reliable and can be applied to investigate targeted fluorescence probes in tumor margin detection. Following this proposed pipeline, BLZ-100 shows enhancement in both tumor cores and tumor margins (mean target-to-background ratio: 8.64 AE 5.76 and 4.82 AE 2.79, respectively).

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Biophotonic technologies for assessment of breast tumor surgical margins—A review

Balasundaram

Krafft

Zhang

et al. 2020

Journal of Biophotonics

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Breast conserving surgery (BCS) offering similar surgical outcomes as mastectomy while retaining breast cosmesis is becoming increasingly popular for the management of early stage breast cancers. However, its association with reoperation rates of 20% to 40% following incomplete tumor removal warrants the need for a fast and accurate intraoperative surgical margin assessment tool that offers cellular, structural and molecular information of the whole specimen surface to a clinically relevant depth. Biophotonic technologies are evolving to qualify as such an intraoperative tool for clinical assessment of breast cancer surgical margins at the microscopic and macroscopic scale. Herein, we review the current research in the application of biophotonic technologies such as photoacoustic imaging, Raman spectroscopy, multimodal multiphoton imaging, diffuse optical imaging and fluorescence imaging using medically approved dyes for breast cancer detection and/or tumor subtype differentiation toward intraoperative assessment of surgical margins in BCS specimens, and possible challenges in their route to clinical translation.

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Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment

Cited by 17 publications

References 32 publications

Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning

Real-time diagnosis and visualization of tumor margins in excised breast specimens using fluorescence lifetime imaging and machine learning

Calibration of fluorescence imaging for tumor surgical margin delineation: multistep registration of fluorescence and histological images

Biophotonic technologies for assessment of breast tumor surgical margins—A review

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