Improving Skin Lesion Segmentation with Generative Adversarial Networks

This paper proposes a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the image segmentation field, and a Convolutional-Deconvolutional Neural Network (CDNN) to automatically generate lesion segmentation mask from dermoscopic images. Training the CDNN with our GAN generated data effectively improves the state-of-the-art

Long-Range 3D Self-Attention for MRI Prostate Segmentation

The problem of prostate segmentation from Magnetic Resonance Imaging (MRI) is an intense research area, due to the increased use of MRI in the diagnosis and treatment planning of prostate cancer. The lack of clear boundaries and huge variation of texture and shapes between patients makes the task very challenging, and the 3D nature of the data makes 2D segmentation algorithms suboptimal for the task. With this paper, we propose a novel architecture to fill the gap between the most recent advances in 2D computer vision and 3D semantic segmentation. In particular, the designed model retrieves multi-scale 3D features with dilated convolutions and makes use of a self-attention transformer to gain a global field of view. The proposed Long-Range 3D Self-Attention block allows the convolutional neural network to build significant features by merging together contextual information collected at various scales. Experimental results show that the proposed method improves the state-of-the-art segmentation accuracy on MRI prostate segmentation

How does Connected Components Labeling with Decision Trees perform on GPUs?

In this paper the problem of Connected Components Labeling (CCL) in binary images using Graphic Processing Units (GPUs) is tackled by a different perspective. In the last decade, many novel algorithms have been released, specifically designed for GPUs. Because CCL literature concerning sequential algorithms is very rich, and includes many efficient solutions, designers of parallel algorithms were often inspired by techniques that had already proved successful in a sequential environment, such as the Union-Find paradigm for solving equivalences between provisional labels. However, the use of decision trees to minimize memory accesses, which is one of the main feature of the best performing sequential algorithms, was never taken into account when designing parallel CCL solutions. In fact, branches in the code tend to cause thread divergence, which usually leads to inefficiency. Anyway, this consideration does not necessarily apply to every possible scenario. Are we sure that the advantages of decision trees do not compensate for the cost of thread divergence? In order to answer this question, we chose three well-known sequential CCL algorithms, which employ decision trees as the cornerstone of their strategy, and we built a data-parallel version of each of them. Experimental tests on real case datasets show that, in most cases, these solutions outperform state-of-the-art algorithms, thus demonstrating the effectiveness of decision trees also in a parallel environment

Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval

In recent years, many attempts have been dedicated to the creation of automated devices that could assist both expert and beginner dermatologists towards fast and early diagnosis of skin lesions. Tasks such as skin lesion classification and segmentation have been extensively addressed with deep learning algorithms, which in some cases reach a diagnostic accuracy comparable to that of expert physicians. However, the general lack of interpretability and reliability severely hinders the ability of those approaches to actually support dermatologists in the diagnosis process. In this paper a novel skin image retrieval system is presented, which exploits features extracted by Convolutional Neural Networks to gather similar images from a publicly available dataset, in order to assist the diagnosis process of both expert and novice practitioners. In the proposed framework, ResNet-50 is initially trained for the classification of dermoscopic images; then, the feature extraction part is isolated, and an embedding network is built on top of it. The embedding learns an alternative representation, which allows to check image similarity by means of a distance measure. Experimental results reveal that the proposed method is able to select meaningful images, which can effectively boost the classification accuracy of human dermatologists

Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification

With this work we tackle immunofluorescence classification in renal biopsy, employing state-of-the-art Convolutional Neural Networks. In this setting, the aim of the probabilistic model is to assist an expert practitioner towards identifying the location pattern of antibody deposits within a glomerulus. Since modern neural networks often provide overconfident outputs, we stress the importance of having a reliable prediction, demonstrating that Temperature Scaling (TS), a recently introduced re-calibration technique, can be successfully applied to immunofluorescence classification in renal biopsy. Experimental results demonstrate that the designed model yields good accuracy on the specific task, and that TS is able to provide reliable probabilities, which are highly valuable for such a task given the low inter-rater agreement

A Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal

In recent years, deep learning has been employed in several medical fields, achieving impressive results. Unfortunately, these algorithms require a huge amount of annotated data to ensure the correct learning process. When dealing with medical imaging, collecting and annotating data can be cumbersome and expensive. This is mainly related to the nature of data, often three-dimensional, and to the need for well-trained expert technicians. In maxillofacial imagery, recent works have been focused on the detection of the Inferior Alveolar Nerve (IAN), since its position is of great relevance for avoiding severe injuries during surgery operations such as third molar extraction or implant installation. In this work, we introduce a novel tool for analyzing and labeling the alveolar nerve from Cone Beam Computed Tomography (CBCT) 3D volumes

A deep analysis on high resolution dermoscopic image classification

[EN] Convolutional neural networks (CNNs) have been broadly employed in dermoscopic image analysis, mainly as a result of the large amount of data gathered by the International Skin Imaging Collaboration (ISIC). As in many other medical imaging domains, state-of-the-art methods take advantage of architectures developed for other tasks, frequently assuming full transferability between enormous sets of natural images (e.g. ImageNet) and dermoscopic images, which is not always the case. A comprehensive analysis on the effectiveness of state-of-the-art deep learning techniques when applied to dermoscopic image analysis is provided. To achieve this goal, the authors consider several CNNs architectures and analyse how their performance is affected by the size of the network, image resolution, data augmentation process, amount of available data, and model calibration. Moreover, taking advantage of the analysis performed, a novel ensemble method to further increase the classification accuracy is designed. The proposed solution achieved the third best result in the 2019 official ISIC challenge, with an accuracy of 0.593.Juan Maroñas is supported by grant FPI-UPV, grant agreement No 825,111 DeepHealth Project, and by the Spanish National Ministry of Education through grant RTI2018-098091-B-I00. The research leading to these results has received funding from the European Union through Programa Operativo del Fondo Europeo de Desarrollo Regional (FEDER) from Comunitat Valencia (2014-2020) under project Sistemas de frabricación inteligentes para la indústria 4.0 (grant agreement IDIFEDER/2018/025).Pollastri, F.; Parreño Lara, M.; Maroñas-Molano, J.; Bolelli, F.; Paredes Palacios, R.; Ramos, D.; Grana, C. (2021). A deep analysis on high resolution dermoscopic image classification. IET Computer Vision. 15(7):514-526. https://doi.org/10.1049/cvi2.1204851452615

Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks

Background and objectives: Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective tool in the elaboration of medical imaging. We wanted to evaluate the role of a convolutional neural network as a support tool for kidney immunofluorescence reporting. Design, setting, participants, & measurements: High-magnification ( 7400) immunofluorescence images of kidney biopsies performed from the year 2001 to 2018 were collected. The report, adopted at the Division of Nephrology of the AOU Policlinico di Modena, describes the specimen in terms of \u201cappearance,\u201d \u201cdistribution,\u201d \u201clocation,\u201d and \u201cintensity\u201d of the glomerular deposits identified with fluorescent antibodies against IgG, IgA, IgM, C1q and C3 complement fractions, fibrinogen, and \u3ba- and \u3bb-light chains. The report was used as ground truth for the training of the convolutional neural networks. Results: In total, 12,259 immunofluorescence images of 2542 subjects undergoing kidney biopsy were collected. The test set analysis showed accuracy values between 0.79 (\u201cirregular capillary wall\u201d feature) and 0.94 (\u201cfine granular\u201d feature). The agreement test of the results obtained by the convolutional neural networks with respect to the ground truth showed similar values to three pathologists of our center. Convolutional neural networks were 117 times faster than human evaluators in analyzing 180 test images. A web platform, where it is possible to upload digitized images of immunofluorescence specimens, is available to evaluate the potential of our approach. Conclusions: The data showed that the accuracy of convolutional neural networks is comparable with that of pathologists experienced in the field

Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes

Inferior Alveolar Nerve (IAN) canal detection has been the focus of multiple recent works in dentistry and maxillofacial imaging. Deep learning-based techniques have reached interesting results in this research field, although the small size of 3D maxillofacial datasets has strongly limited the performance of these algorithms. Researchers have been forced to build their own private datasets, thus precluding any opportunity for reproducing results and fairly comparing proposals. This work describes a novel, large, and publicly available mandibular Cone Beam Computed Tomography (CBCT) dataset, with 2D and 3D manual annotations, provided by expert clinicians. Leveraging this dataset and employing deep learning techniques, we are able to improve the state of the art on the 3D mandibular canal segmentation. The source code which allows to exactly reproduce all the reported experiments is released as an open-source project, along with this article