Automatic Optic Nerve Measurement: A New Tool to Standardize Optic Nerve Assessment in Ultrasound B-Mode Images

Transorbital sonography provides reliable information about the estimation of intra-cranial pressure by measuring the optic nerve sheath diameter (ONSD), whereas the optic nerve (ON) diameter (OND) may reveal ON atrophy in patients with multiple sclerosis. Here, an AUTomatic Optic Nerve MeAsurement (AUTONoMA) system for OND and ONSD assessment in ultrasound B-mode images based on deformable models is presented. The automated measurements were compared with manual ones obtained by two operators, with no significant differences. AUTONoMA correctly segmented the ON and its sheath in 71 out of 75 images. The mean error compared with the expert operator was 0.06 ± 0.52 mm and 0.06 ± 0.35 mm for the ONSD and OND, respectively. The agreement between operators and AUTONoMA was good and a positive correlation was found between the readers and the algorithm with errors comparable with the inter-operator variability. The AUTONoMA system may allow for standardization of OND and ONSD measurements, reducing manual evaluation variability

Multimodal T2w and DWI Prostate Gland Automated Registration

Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre-and post-registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI

OD26 - Inverse consistency error as a validation metric for deformable image registration: preliminary implementation research

The aim of this work is to develop a novel automatic voxel-based quantitative measurement approach to evaluate the registration accuracy of a Deformable Image Registration (DIR) algorithm in clinical practice. As the Inverse Consistency Error (ICE) can be computed directly from the deformation vector field (DVF) generated by the Treatment Planning System (TPS), it appears to be a valid surrogate of standard quality assurance metrics to assess the spatial error in the registration process

Automatic Extraction of Dermatological Parameters from Nevi Using an Inexpensive Smartphone Microscope: A Proof of Concept

The evolution of smartphone technology has made their use more common in dermatological applications. Here we studied the feasibility of using an inexpensive smartphone microscope for the extraction of dermatological parameters and compared the results obtained with a portable dermoscope, commonly used in clinical practice. Forty-two skin lesions were imaged with both devices and visually analyzed by an expert dermatologist. The presence of a reticular pattern was observed in 22 dermoscopic images, but only in 10 smartphone images. The proposed paradigm segments the image and extracts texture features which are used to train and validate a neural network to classify the presence of a reticular pattern. Using 5-fold cross-validation, an accuracy of 100% and 95% was obtained with the dermoscopic and smartphone images, respectively. This approach can be useful for general practitioners and as a triage tool for skin lesion analysis

Stain Color Adaptive Normalization (SCAN) algorithm: Separation and standardization of histological stains in digital pathology

Background and objective: The diagnosis of histopathological images is based on the visual analysis of tissue slices under a light microscope. However, the histological tissue appearance may assume different color intensities depending on the staining process, operator ability and scanner specifications. This stain variability affects the diagnosis of the pathologist and decreases the accuracy of computer-aided diagnosis systems. In this context, the stain normalization process has proved to be a powerful tool to cope with this issue, allowing to standardize the stain color appearance of a source image respect to a reference image. Methods: In this paper, novel fully automated stain separation and normalization approaches for hematoxylin and eosin stained histological slides are presented. The proposed algorithm, named SCAN (Stain Color Adaptive Normalization), is based on segmentation and clustering strategies for cellular structures detection. The SCAN algorithm is able to improve the contrast between histological tissue and background and preserve local structures without changing the color of the lumen and the background. Results: Both stain separation and normalization techniques were qualitatively and quantitively validated on a multi-tissue and multiscale dataset, with highly satisfactory results, outperforming the state-of-the-art approaches. SCAN was also tested on whole-slide images with high performances and low computational times. Conclusions: The potential contribution of the proposed standardization approach is twofold: the improvement of visual diagnosis in digital histopathology and the development of powerful pre-processing strategies to automated classification techniques for cancer detection

MO-03.8 - INVERSE CONSISTENCY ERROR AS A VALIDATION METRIC FOR DEFORMABLE IMAGE REGISTRATION: IMPLEMENTATION RESEARCH WITH DIFFERENT COMPUTATIONAL PHANTOMS

This work aims to apply the Inverse Consistency Error (ICE) metric on deformable vector fields (DVFs) to evaluate the deformable image registration (DIR) spatial uncertainties in the context of dose warping applications

Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study

In clinical routine, the quality of whole-slide images plays a key role in the pathologist’s diagnosis, and suboptimal staining may be a limiting factor. The stain normalization process helps to solve this problem through the standardization of color appearance of a source image with respect to a target image with optimal chromatic features. The analysis is focused on the evaluation of the following parameters assessed by two experts on original and normalized slides: (i) perceived color quality, (ii) diagnosis for the patient, (iii) diagnostic confidence and (iv) time required for diagnosis. Results show a statistically significant increase in color quality in the normalized images for both experts (p < 0.0001). Regarding prostate cancer assessment, the average times for diagnosis are significantly lower for normalized images than original ones (first expert: 69.9 s vs. 77.9 s with p < 0.0001; second expert: 37.4 s vs. 52.7 s with p < 0.0001), and at the same time, a statistically significant increase in diagnostic confidence is proven. The improvement of poor-quality images and greater clarity of diagnostically important details in normalized slides demonstrate the potential of stain normalization in the routine practice of prostate cancer assessment