combining-reflectance-confocal-microscopy-with-optical-coherence

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Skin cancer is one of the most common cancers worldwide. Diagnosis relies on visual inspection and dermoscopy followed by biopsy for histopathological ...

Memorial Sloan Kettering Cancer Center

Complete visualization of epidermal margin during ex vivo confocal microscopy of excised tissue with 3-dimensional mosaicking and intensity projection.

Portable confocal microscopy (PCM) is a low-cost reflectance confocal microscopy technique that can visualize cellular details of human skin in vivo. When PCM images are acquired with a short exposure time to reduce motion blur and enable real-time 3D imaging, the signal-to-noise ratio (SNR) is decreased significantly, which poses challenges in reliably analyzing cellular features. In this paper, we evaluated deep learning (DL)-based approach for reducing noise in PCM images acquired with a short exposure time.

Deep Learning-Based Denoising in High-Speed Portable Reflectance Confocal Microscopy.

Combined reflectance confocal microscopy and optical coherence tomography to improve the diagnosis of equivocal lesions for basal cell carcinoma.

Ex vivo confocal microscopy (EVCM) generates digitally colored purple-pink images similar to H&E without time-consuming tissue processing. It can be used during Mohs surgery for rapid detection of basal cell carcinoma (BCC); however, reading EVCM images requires specialized training. An automated approach using a deep learning algorithm for BCC detection in EVCM images can aid in diagnosis. A total of 40 BCCs and 28 negative (not-BCC) samples were collected at Memorial Sloan Kettering Cancer Center to create three training datasets: (i) EVCM image dataset (663 images), (ii) H&E image dataset (516 images), and (iii) a combination of the two datasets. A total of seven BCCs and four negative samples were collected to create an EVCM test dataset (107 images). The model trained with the EVCM dataset achieved 92% diagnostic accuracy, similar to the H&E model (93%). The area under the receiver operator characteristic curve was 0.94, 0.95, and 0.94 for EVCM-, H&E-, and combination-trained models, respectively. We developed an algorithm for automatic BCC detection in EVCM images (comparable accuracy to dermatologists). This approach could be used to assist with BCC detection during Mohs surgery. Furthermore, we found that a model trained with only H&E images (which are more available than EVCM images) can accurately detect BCC in EVCM images.

Ucalene Harris

Dermatology Service,
Department of Medicine,
Dermatology Service, Department of Medicine

Memorial Sloan Kettering Cancer Center

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Ucalene Harris

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Dermatology Service,Department of Medicine,Dermatology Service, Department of Medicine

Memorial Sloan Kettering Cancer Center

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Dermatology Service,
Department of Medicine,
Dermatology Service, Department of Medicine