In this study, we propose to combine miniaturized optical coherence tomography (OCT) catheter with a residual neural network (ResNet)-based deep learning model for differentiation of normal from cancerous colorectal tissue in fresh ex vivo specimens. The OCT catheter has an outer diameter of 3.8 mm, a lateral resolution of ~10 um, and an axial resolution of 6 um. A customized ResNet-based neural network structure was trained on both benchtop and catheter images. An AUC of 0.97 was achieved to distinguish between normal and cancerous colorectal tissue when testing on the rest of catheter images.
In this study, we propose to combine miniaturized optical coherence tomography (OCT) catheter with pattern recognition (PR) OCT for differentiation of normal from neoplastic colorectal tissue in real-time. The OCT catheter has a lateral resolution of 17.15 um and an axial resolution of 6 um. The PR-OCT system is trained by RetinaNet for pattern recognition tasks. Our method leverages the recent advancement in object detection, which localizes and classifies the diagnostic features at real-time, and the integration of an endoscopy, which promises future in vivo studies. According to our previous reports, a sensitivity of 100% and specificity of 99.7% can be reached.
A machine learning (ML) model with physical constraints is introduced to perform diffuse optical tomography (DOT) reconstruction. Here, for the first time, we combine ultrasound-guided DOT with ML to facilitate DOT reconstruction. Our method has two key components: (i) An unsupervised auto-encoder with transfer learning is adopted for clinical data without a ground truth, and (ii) physical constraints are implemented to achieve accurate reconstruction. Both qualitative and quantitative results demonstrate that the accuracy of the proposed method surpasses that of the existing model. In a phantom study, compared with the Born conjugate gradient descent (CGD) reconstruction method, the ML method improves the reconstructed maximum absorption coefficient by 18.3% on high contrast phantom and by 61.3% on low contrast phantom, with improved depth distribution of absorption maps. In a clinical study, better contrast was obtained from a treated breast cancer pre- and post- treatment.
In this ex vivo study, we report the first use of texture features and computer vision-based image features acquired from en face scattering coefficient maps to diagnose colorectal diseases. From these maps, texture features were extracted from a gray-level co-occurrence matrix algorithm, and computer vision-based image features were derived using a scale-invariant feature transform algorithm. Twenty-five features were obtained and thirty-three patients were recruited. Machine learning models were trained using an optimal feature set. The trained models achieved 94.7% sensitivity and 94.0% specificity for differentiating abnormal from normal, and 86.9% sensitivity and 85.0% specificity when distinguishing adenomatous polyp from cancer.
Endoscopic evaluation of the colorectum is limited to the mucosal surface and provides no functional or structural information regarding subsurface changes. Targeted diagnostics and individualized treatment, however, requires this information. In this ex vivo study of human colorectal tissue, we use swept-source optical coherence tomography to create quantified subsurface scattering coefficient maps of normal and cancerous tissue. Specifically, we use a novel wavelet-based-curve-fitting method to generate subsurface scattering coefficient maps. The angular spectra of scattering coefficient maps of normal tissues exhibit a spatial feature distinct from those of abnormal tissues. The en face scattering coefficient maps of the normal colon contain a large area of homogenous scattering coefficients with periodic dot patterns, while the scattering coefficient map of cancer region shows a large area of heterogeneous scattering coefficients. An angular spectrum index to quantify the differences between the normal, abnormal, and treated tissues is derived, and its strength in revealing subsurface cancer in ex vivo samples is statistically analyzed. Using this index, we differentiate malignant from normal colonic tissue. Additionally, we also found that rectal cancers completely destroyed by preoperative treatment appear much more similarly to normal tissue than the original malignancy. The study demonstrates that the angular spectrum of the scattering coefficient map can effectively reveal subsurface colorectal cancer and help clinicians identify patients who don’t require surgical intervention.
A multi-spectral, portable, hand-held LED based spatial frequency domain imaging system was used for ex vivo imaging pretreatment and post treatment human colon and rectal tissues. Freshly excised human colon and rectal tissue samples were imaged with the hand-held SFDI probe with 9 wavelengths extending from visible to NIR (660-950 nm). Important tumor biomarkers such as hemoglobin, scatter amplitude, scatter spectral slope, water and lipid content were quantitatively extracted from the SFDI absorption and scattering images. Significant differences were observed between the absorption as well as scattering distribution of normal, tumor and polyp tissue as well as between pretreated and post-treated tumors.
It is important to provide timely information to surgeons on diagnosis of a suspicious ovarian tissue before excision to avoid unnecessary surgery, especially for young women. In this report, we introduce a new 3-D surface mapping technique to map ovarian tissue scattering properties by fitting the swept-source optical coherence tomography (SS-OCT) signals to a scattering model. We observed that lower scattering coefficients and heterogeneous spatial distribution were associated with malignant ovarian tissues, and higher scattering coefficients and homogeneous spatial distribution indicated benign ovarian tissues. The initial results suggest that the 3-D scattering map has potential to be an effective tool to characterize normal and malignant ovarian tissues.
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