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

Abstract

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).

Off-Peak Near-Infrared-II (NIR-II) Bioimaging of an Immunoconjugate Having Peak Fluorescence Emission in the NIR-I Spectral Region for Improving Tumor Margin Delineation

“…The primary modality for treating most malignant tumors continues to entail maximal surgical resection of the diseased tissue, wherein the approach is to remove all cancer tissue/cells such that the patient would be cured of cancer. − The presence or absence of residual tumor tissue/cells in the 2–10 mm area beyond that which is resected is referred to as the surgical tumor margin. ,, The presence or absence of residual tumor tissue/cells at the tumor margin is considered to be one of the strongest predictors of recurrence and survival. ,− Positive margins are the result of tumor tissue/cells remaining at part or all of such perimeter after surgery, wherein the presence of residual diseased tissue/cells is associated with increased local recurrence and provides poor prognoses for the cancer patient. − Any postoperative salvage surgery in attempts to remove residual diseased tissue yields poor outcomes . Although the primary goal of cancer surgery is to cure the patient, preservation of important anatomical structures is very important as well for achieving optimal patient outcome.…”

Intensity-based registration of bright-field and second-harmonic generation images of histopathology tissue sections

“…Secondly, BF histology images are dominated by low-level features with high co-occurrence such as cellular and stromal textures which can make the matching of the descriptors with a sparse image such as SHG image of fibrillar collagen very difficult [23,24]. Thus, extrinsic features such as fiducial markers or distinguishing outlines of the samples often need to be present in order to apply this family of approaches to cross-modality image registration [25][26][27]. On the other hand, intensity-based approaches widely applied to medical image registration [28][29][30][31][32][33] are free from the above limitations because they do not deal with the identification of local geometrical landmarks and instead, find a transformation that maximizes the global measurement of similarity.…”