Optical technologies revolutionized the field of oncologic surgery in recent years by providing non-invasive and innovative ways to monitor the assessment of resection margins during surgical procedures. By providing real-time visualization of tissue characteristics, optical technologies, such as diffuse reflectance spectroscopy (DRS),^1,2 fluorescence lifetime imaging,³ and hyperspectral imaging,^4,5 can help to assess if all cancerous tissues are removed while minimizing damage to surrounding healthy tissues. This will lower the number of positive resection margins and thereby reduce the need for additional treatments, such as surgical re-excision or radiotherapy. However, the accuracy and reliability of these technologies are crucial to ensure safety and efficacy during surgical procedures. Therefore, validation of optical technologies is essential to establish their clinical significance and provide healthcare professionals with the confidence to use them in their practice. This involves assessing the clinical outcomes of these technologies with the current ground truth.⁶

The validation of newly developed optical tissue-sensing techniques for tumor detection during cancer surgery requires an accurate correlation with the histological results. Additionally, such an accurate correlation facilitates precise data labeling for developing high-performance machine learning tissue-classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with the histopathology results is proposed and validated on a case study. The proposed framework provides a more-robust and accurate method for the tracking and validation of optical tissue-sensing techniques, which saves time and resources compared to the available conventional techniques.

Vacuum-assisted breast biopsy (VABB), widely used for diagnosing breast cancer, is prone to sampling errors that may necessitate repeat procedures and dela

Validation of newly developed optical tissue sensing techniques for tumor detection during cancer surgery requires an accurate correlation with histological results. Additionally, such accurate correlation facilitates precise data labeling for developing high-performance machine-learning tissue classification models. In this paper, a newly developed Point Projection Mapping system will be introduced, which allows non-destructive tracking of the measurement locations on tissue specimens. Additionally, a framework for accurate registration, validation, and labeling with histopathology results is proposed and validated on a case study. The proposed framework provides a more robust and accurate method for tracking and validation of optical tissue sensing techniques, which saves time and resources compared to conventional techniques available.

As breast cancer care advances toward increasingly personalized treatment, the demand for highly accurate, minimally invasive diagnostic methods has never been greater. Histopathological confirmati...

The correlation of optical measurements with a correct pathology label is often hampered by imprecise registration caused by deformations in histology images. This study explores an automated multi-modal image registration technique utilizing deep learning principles to align snapshot breast specimen images with corresponding histology images. The input images, acquired through different modalities, present challenges due to variations in intensities and structural visibility, making linear assumptions inappropriate. An unsupervised and supervised learning approach, based on the VoxelMorph model, was explored, making use of a dataset with manually registered images used as ground truth. Evaluation metrics, including Dice scores and mutual information, reveal that the unsupervised model outperforms the supervised (and manual approach) significantly, achieving superior image alignment. This automated registration approach holds promise for improving the validation of optical technologies by minimizing human errors and inconsistencies associated with manual registration.