Automatic pigmented lesion segmentation through a dermoscopy-guided OCT approach for early diagnosis

Early diagnosis of pigmented lesions, specially melanoma, is an unmet clinical need that would help to improve patient prognosis. Apart from histopathological biopsy, the only gold standard non-invasive imaging technique during diagnosis is dermatoscopy (DD). Over the last years, new medical imaging techniques are being developed and Optical Coherence Tomography (OCT) has demonstrated to be very helpful on dermatology. OCT is non-invasive and provides in-depth structural microscopic information of the skin in real-time. In comparison with other novel techniques, as Reflectance Confocal Microscopy (RCM), the acquisition time is lower and the field-of-view higher. Hence, consolidated diagnosis techniques and novel imaging modalities can be combined to improve decision making during diagnosis and treatment. With actual methods, the delineation of lesion margins directly on OCT images during early stages of the disease is still really challenging and, at the same time, relevant from a prognosis perspective. This work proposes combining DD and OCT images to take advantage of their complementary information. The goal is to guide lesions delineation on OCT images considering the clinical features on DD images. The developed method applies image processing techniques to DD image to automatically segment the lesion; later, and after a calibration procedure, DD and OCT images become coregistered. In a final step the DD segmentation is transferred into the OCT image. Applying advanced image processing techniques and the proposed strategy of lesion delimitation, histopathological characteristics of the segmented lesion can be studied on OCT images afterwards. This proposal can lead to early, real-time and non-invasive diagnosis of pigmented lesions.This work has been developed thanks to the funding of the ECSEL European project ASTONISH (ID.692470) and Basque Country (Spain) ELKARTEK projects MELAMICS (KK-2016-00036) and MELAMICS II (KK-2017/00041). Special thanks to the dermatologists and personnel of the Cruces University Hospital (Cruces, Spain) and the Basurto University Hospital (Bilbao, Spain) for their collaboration on the generation of the annotated database from real patients

Automatic pigmented lesion segmentation through a dermoscopy-guided OCT approach for early diagnosis

Early diagnosis of pigmented lesions, specially melanoma, is an unmet clinical need that would help to improve patient prognosis. Apart from histopathological biopsy, the only gold standard non-invasive imaging technique during diagnosis is dermatoscopy (DD). Over the last years, new medical imaging techniques are being developed and Optical Coherence Tomography (OCT) has demonstrated to be very helpful on dermatology. OCT is non-invasive and provides in-depth structural microscopic information of the skin in real-time. In comparison with other novel techniques, as Reflectance Confocal Microscopy (RCM), the acquisition time is lower and the field-of-view higher. Hence, consolidated diagnosis techniques and novel imaging modalities can be combined to improve decision making during diagnosis and treatment. With actual methods, the delineation of lesion margins directly on OCT images during early stages of the disease is still really challenging and, at the same time, relevant from a prognosis perspective. This work proposes combining DD and OCT images to take advantage of their complementary information. The goal is to guide lesions delineation on OCT images considering the clinical features on DD images. The developed method applies image processing techniques to DD image to automatically segment the lesion; later, and after a calibration procedure, DD and OCT images become coregistered. In a final step the DD segmentation is transferred into the OCT image. Applying advanced image processing techniques and the proposed strategy of lesion delimitation, histopathological characteristics of the segmented lesion can be studied on OCT images afterwards. This proposal can lead to early, real-time and non-invasive diagnosis of pigmented lesions.This work has been developed thanks to the funding of the ECSEL European project ASTONISH (ID.692470) and Basque Country (Spain) ELKARTEK projects MELAMICS (KK-2016-00036) and MELAMICS II (KK-2017/00041). Special thanks to the dermatologists and personnel of the Cruces University Hospital (Cruces, Spain) and the Basurto University Hospital (Bilbao, Spain) for their collaboration on the generation of the annotated database from real patients

Deep-Learning-Based Automatic Detection and Segmentation of Brain Metastases with Small Volume for Stereotactic Ablative Radiotherapy

Recently, several efforts have been made to develop the deep learning (DL) algorithms for automatic detection and segmentation of brain metastases (BM). In this study, we developed an advanced DL model to BM detection and segmentation, especially for small-volume BM. From the institutional cancer registry, contrast-enhanced magnetic resonance images of 65 patients and 603 BM were collected to train and evaluate our DL model. Of the 65 patients, 12 patients with 58 BM were assigned to test-set for performance evaluation. Ground-truth for BM was assigned to one radiation oncologist to manually delineate BM and another one to cross-check. Unlike other previous studies, our study dealt with relatively small BM, so the area occupied by the BM in the high-resolution images were small. Our study applied training techniques such as the overlapping patch technique and 2.5-dimensional (2.5D) training to the well-known U-Net architecture to learn better in smaller BM. As a DL architecture, 2D U-Net was utilized by 2.5D training. For better efficacy and accuracy of a two-dimensional U-Net, we applied effective preprocessing include 2.5D overlapping patch technique. The sensitivity and average false positive rate were measured as detection performance, and their values were 97% and 1.25 per patient, respectively. The dice coefficient with dilation and 95% Hausdorff distance were measured as segmentation performance, and their values were 75% and 2.057 mm, respectively. Our DL model can detect and segment BM with small volume with good performance. Our model provides considerable benefit for clinicians with automatic detection and segmentation of BM for stereotactic ablative radiotherapy.ope

An analysis of rotation matrix and colour constancy data augmentation in classifying images of animals

In this paper, we examine a novel data augmentation (DA) method that transforms an image into a new image containing multiple rotated copies of the original image. The DA method creates a grid of cells, in which each cell contains a different randomly rotated image and introduces a natural background in the newly created image. We investigate the use of deep learning to assess the classification performance on the rotation matrix or original dataset with colour constancy versions of the datasets. For the colour constancy methods, we use two well-known retinex techniques: the multi-scale retinex and the multi-scale retinex with colour restoration for enhancing both original (ORIG) and rotation matrix (ROT) images. We perform experiments on three datasets containing images of animals, from which the first dataset is collected by us and contains aerial images of cows or non-cow backgrounds. To classify the Aerial UAV images, we use a convolutional neural network (CNN) architecture and compare two loss functions (hinge loss and cross-entropy loss). Additionally, we compare the CNN to classical feature-based techniques combined with a k-nearest neighbour classifier or a support vector machine. The best approach is then used to examine the colour constancy DA variants, ORIG and ROT-DA alone for three datasets (Aerial UAV, Bird-600 and Croatia fish). The results show that the rotation matrix data augmentation is very helpful for the Aerial UAV dataset. Furthermore, the colour constancy data augmentation is helpful for the Bird-600 dataset. Finally, the results show that the fine-tuned CNNs significantly outperform the CNNs trained from scratch on the Croatia fish and the Bird-600 datasets, and obtain very high accuracies on the Aerial UAV and Bird-600 datasets

Hsc70-4 Deforms Membranes to Promote Synaptic Protein Turnover by Endosomal Microautophagy

SummarySynapses are often far from their cell bodies and must largely independently cope with dysfunctional proteins resulting from synaptic activity and stress. To identify membrane-associated machines that can engulf synaptic targets destined for degradation, we performed a large-scale in vitro liposome-based screen followed by functional studies. We identified a presynaptically enriched chaperone Hsc70-4 that bends membranes based on its ability to oligomerize. This activity promotes endosomal microautophagy and the turnover of specific synaptic proteins. Loss of microautophagy slows down neurotransmission while gain of microautophagy increases neurotransmission. Interestingly, Sgt, a cochaperone of Hsc70-4, is able to switch the activity of Hsc70-4 from synaptic endosomal microautophagy toward chaperone activity. Hence, Hsc70-4 controls rejuvenation of the synaptic protein pool in a dual way: either by refolding proteins together with Sgt, or by targeting them for degradation by facilitating endosomal microautophagy based on its membrane deforming activity