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FEDERICO POLLASTRI
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Pubblicazioni
2023
- Inferior Alveolar Canal Automatic Detection with Deep Learning CNNs on CBCTs: Development of a Novel Model and Release of Open-Source Dataset and Algorithm
[Articolo su rivista]
Di Bartolomeo, Mattia; Pellacani, Arrigo; Bolelli, Federico; Cipriano, Marco; Lumetti, Luca; Negrello, Sara; Allegretti, Stefano; Minafra, Paolo; Pollastri, Federico; Nocini, Riccardo; Colletti, Giacomo; Chiarini, Luigi; Grana, Costantino; Anesi, Alexandre
abstract
Introduction: The need of accurate three-dimensional data of anatomical structures is increasing in the surgical field. The development of convolutional neural networks (CNNs) has been helping to fill this gap by trying to provide efficient tools to clinicians. Nonetheless, the lack of a fully accessible datasets and open-source algorithms is slowing the improvements in this field. In this paper, we focus on the fully automatic segmentation of the Inferior Alveolar Canal (IAC), which is of immense interest in the dental and maxillo-facial surgeries. Conventionally, only a bidimensional annotation of the IAC is used in common clinical practice. A reliable convolutional neural network (CNNs) might be timesaving in daily practice and improve the quality of assistance. Materials and methods: Cone Beam Computed Tomography (CBCT) volumes obtained from a single radiological center using the same machine were gathered and annotated. The course of the IAC was annotated on the CBCT volumes. A secondary dataset with sparse annotations and a primary dataset with both dense and sparse annotations were generated. Three separate experiments were conducted in order to evaluate the CNN. The IoU and Dice scores of every experiment were recorded as the primary endpoint, while the time needed to achieve the annotation was assessed as the secondary end-point. Results: A total of 347 CBCT volumes were collected, then divided into primary and secondary datasets. Among the three experiments, an IoU score of 0.64 and a Dice score of 0.79 were obtained thanks to the pre-training of the CNN on the secondary dataset and the creation of a novel deep label propagation model, followed by proper training on the primary dataset. To the best of our knowledge, these results are the best ever published in the segmentation of the IAC. The datasets is publicly available and algorithm is published as open-source software. On average, the CNN could produce a 3D annotation of the IAC in 6.33 s, compared to 87.3 s needed by the radiology technician to produce a bidimensional annotation. Conclusions: To resume, the following achievements have been reached. A new state of the art in terms of Dice score was achieved, overcoming the threshold commonly considered of 0.75 for the use in clinical practice. The CNN could fully automatically produce accurate three-dimensional segmentation of the IAC in a rapid setting, compared to the bidimensional annotations commonly used in the clinical practice and generated in a time-consuming manner. We introduced our innovative deep label propagation method to optimize the performance of the CNN in the segmentation of the IAC. For the first time in this field, the datasets and the source codes used were publicly released, granting reproducibility of the experiments and helping in the improvement of IAC segmentation.
2022
- Applications of AI and HPC in the Health Domain
[Capitolo/Saggio]
Oniga, D.; Cantalupo, B.; Tartaglione, E.; Perlo, D.; Grangetto, M.; Aldinucci, M.; Bolelli, F.; Pollastri, F.; Cancilla, M.; Canalini, L.; Grana, C.; Alcalde, C. M.; Cardillo, F. A.; Florea, M.
abstract
2022
- Automated Prediction of Kidney Failure in IgA Nephropathy with Deep Learning from Biopsy Images
[Articolo su rivista]
Testa, F.; Fontana, F.; Pollastri, F.; Chester, J.; Leonelli, M.; Giaroni, F.; Gualtieri, F.; Bolelli, F.; Mancini, E.; Nordio, M.; Sacco, P.; Ligabue, G.; Giovanella, S.; Ferri, M.; Alfano, G.; Gesualdo, L.; Cimino, S.; Donati, G.; Grana, C.; Magistroni, R.
abstract
Background and objectives Digital pathology and artificial intelligence offer new opportunities for automatic histologic scoring. We applied a deep learning approach to IgA nephropathy biopsy images to develop an automatic histologic prognostic score, assessed against ground truth (kidney failure) among patients with IgA nephropathy who were treated over 39 years. We assessed noninferiority in comparison with the histologic component of currently validated predictive tools. We correlated additional histologic features with our deep learning predictive score to identify potential additional predictive features. Design, setting, participants, & measurements Training for deep learning was performed with randomly selected, digitalized, cortical Periodic acid–Schiff–stained sections images (363 kidney biopsy specimens) to develop our deep learning predictive score. We estimated noninferiority using the area under the receiver operating characteristic curve (AUC) in a randomly selected group (95 biopsy specimens) against the gold standard Oxford classification (MEST-C) scores used by the International IgA Nephropathy Prediction Tool and the clinical decision supporting system for estimating the risk of kidney failure in IgA nephropathy. We assessed additional potential predictive histologic features against a subset (20 kidney biopsy specimens) with the strongest and weakest deep learning predictive scores. Results We enrolled 442 patients; the 10-year kidney survival was 78%, and the study median follow-up was 6.7 years. Manual MEST-C showed no prognostic relationship for the endocapillary parameter only. The deep learning predictive score was not inferior to MEST-C applied using the International IgA Nephropathy Prediction Tool and the clinical decision supporting system (AUC of 0.84 versus 0.77 and 0.74, respectively) and confirmed a good correlation with the tubolointerstitial score (r50.41, P,0.01). We observed no correlations between the deep learning prognostic score and the mesangial, endocapillary, segmental sclerosis, and crescent parameters. Additional potential predictive histopathologic features incorporated by the deep learning predictive score included (1)inflammation within areas of interstitial fibrosis and tubular atrophy and (2) hyaline casts. Conclusions The deep learning approach was noninferior to manual histopathologic reporting and considered prognostic features not currently included in MEST-C assessment.
2022
- Deep Segmentation of the Mandibular Canal: a New 3D Annotated Dataset of CBCT Volumes
[Articolo su rivista]
Cipriano, Marco; Allegretti, Stefano; Bolelli, Federico; Di Bartolomeo, Mattia; Pollastri, Federico; Pellacani, Arrigo; Minafra, Paolo; Anesi, Alexandre; Grana, Costantino
abstract
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.
2022
- Improving Segmentation of the Inferior Alveolar Nerve through Deep Label Propagation
[Relazione in Atti di Convegno]
Cipriano, Marco; Allegretti, Stefano; Bolelli, Federico; Pollastri, Federico; Grana, Costantino
abstract
2022
- Long-Range 3D Self-Attention for MRI Prostate Segmentation
[Relazione in Atti di Convegno]
Pollastri, Federico; Cipriano, Marco; Bolelli, Federico; Grana, Costantino
abstract
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.
2021
- A Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal
[Relazione in Atti di Convegno]
Mercadante, Cristian; Cipriano, Marco; Bolelli, Federico; Pollastri, Federico; Di Bartolomeo, Mattia; Anesi, Alexandre; Grana, Costantino
abstract
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.
2021
- A Deep Analysis on High Resolution Dermoscopic Image Classification
[Articolo su rivista]
Pollastri, Federico; Parreño, Mario; Maroñas, Juan; Bolelli, Federico; Paredes, Roberto; Ramos, Daniel; Grana, Costantino
abstract
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).
Like 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 (eg{} ImageNet) and dermoscopic images, which is not always the case.
With this paper we provide a comprehensive analysis on the effectiveness of state-of-the-art deep learning techniques when applied to dermoscopic image analysis. In order to achieve this goal, we consider several CNNs architectures and analyze 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, we design a novel ensemble method to further increase the classification accuracy. The proposed solution achieved the third best result in the 2019 official ISIC challenge, with an accuracy of 0.593.
2021
- Confidence Calibration for Deep Renal Biopsy Immunofluorescence Image Classification
[Relazione in Atti di Convegno]
Pollastri, Federico; Maroñas, Juan; Bolelli, Federico; Ligabue, Giulia; Paredes, Roberto; Magistroni, Riccardo; Grana, Costantino
abstract
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.
2021
- Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval
[Relazione in Atti di Convegno]
Allegretti, Stefano; Bolelli, Federico; Pollastri, Federico; Longhitano, Sabrina; Pellacani, Giovanni; Grana, Costantino
abstract
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.
2020
- Augmenting data with GANs to segment melanoma skin lesions
[Articolo su rivista]
Pollastri, Federico; Bolelli, Federico; Paredes Palacios, Roberto; Grana, Costantino
abstract
This paper presents a novel strategy that employs Generative Adversarial Networks (GANs) to augment data in the skin lesion segmentation task, which is a fundamental first step in the automated melanoma detection process. The proposed framework generates both skin lesion images and their segmentation masks, making the data augmentation process extremely straightforward. In order to thoroughly analyze how the quality and diversity of synthetic images impact the efficiency of the method, we remodel two different well known GANs: a Deep Convolutional GAN (DCGAN) and a Laplacian GAN (LAPGAN). Experimental results reveal that, by introducing such kind of synthetic data into the training process, the overall accuracy of a state-of-the-art Convolutional/Deconvolutional Neural Network for melanoma skin lesion segmentation is increased.
2020
- Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks
[Articolo su rivista]
Ligabue, Giulia; Pollastri, Federico; Fontana, Francesco; Leonelli, Marco; Furci, Luciana; Giovanella, Silvia; Alfano, Gaetano; Cappelli, Gianni; Testa, Francesca; Bolelli, Federico; Grana, Costantino; Magistroni, Riccardo
abstract
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 (×400) 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 “appearance,” “distribution,” “location,” and “intensity” of the glomerular deposits identified with fluorescent antibodies against IgG, IgA, IgM, C1q and C3 complement fractions, fibrinogen, and κ- and λ-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 (“irregular capillary wall” feature) and 0.94 (“fine granular” 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.
2019
- How does Connected Components Labeling with Decision Trees perform on GPUs?
[Relazione in Atti di Convegno]
Allegretti, Stefano; Bolelli, Federico; Cancilla, Michele; Pollastri, Federico; Canalini, Laura; Grana, Costantino
abstract
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.
2019
- Skin Lesion Segmentation Ensemble with Diverse Training Strategies
[Relazione in Atti di Convegno]
Canalini, Laura; Pollastri, Federico; Bolelli, Federico; Cancilla, Michele; Allegretti, Stefano; Grana, Costantino
abstract
This paper presents a novel strategy to perform skin lesion segmentation from dermoscopic images. We design an effective segmentation pipeline, and explore several pre-training methods to initialize the features extractor, highlighting how different procedures lead the Convolutional Neural Network (CNN) to focus on different features.
An encoder-decoder segmentation CNN is employed to take advantage of each pre-trained features extractor.
Experimental results reveal how multiple initialization strategies can be exploited, by means of an ensemble method, to obtain state-of-the-art skin lesion segmentation accuracy.
2018
- Improving Skin Lesion Segmentation with Generative Adversarial Networks
[Relazione in Atti di Convegno]
Pollastri, Federico; Bolelli, Federico; Paredes, Roberto; Grana, Costantino
abstract
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.