Publications by Costantino Grana

Despite their remarkable success in medical image segmentation, the life cycle of deep neural networks remains a challenge in clinical applications. These models must be regularly updated to integrate new medical data and customized to meet evolving diagnostic standards, regulatory requirements, commercial needs, and privacy constraints. Model merging offers a promising solution, as it allows working with multiple specialized networks that can be created and combined dynamically instead of relying on monolithic models. While extensively studied in standard 2D classification, the potential of model merging for 3D segmentation remains unexplored. This paper presents an efficient framework that allows effective model merging in the domain of 3D image segmentation. Our approach builds upon theoretical analysis and encourages wide minima during pre-training, which we demonstrate to facilitate subsequent model merging. Using U-Net 3D, we evaluate the method on distinct anatomical structures with the ToothFairy2 and BTCV Abdomen datasets. To support further research, we release the source code and all the model weights in a dedicated repository: https://github.com/LucaLumetti/UNetTransplant

This article is about Connected Components Labeling (CCL) algorithms developed for GPU accelerators. The task itself is employed in many modern image-processing pipelines and represents a fundamental step in different scenarios, whenever object recognition is required. For this reason, a strong effort in the development of many different proposals devoted to improving algorithm performance using different kinds of hardware accelerators has been made. This paper focuses on GPU-based algorithmic solutions published in the last two decades, highlighting their distinctive traits and the improvements they leverage. The state-of-the-art review proposed is equipped with the source code, which allows to straightforwardly reproduce all the algorithms in different experimental settings. A comprehensive evaluation on multiple environments is also provided, including different operating systems, compilers, and GPUs. Our assessments are performed by means of several tests, including real-case images and synthetically generated ones, highlighting the strengths and weaknesses of each proposal. Overall, the experimental results revealed that block-based oriented algorithms outperform all the other algorithmic solutions on both 2D images and 3D volumes, regardless of the selected environment.

Since their invention in 1949, barcodes have remained the preferred method for automatic data capture, playing a crucial role in supply chain management. To detect a barcode in an image, multiple algorithms have been proposed in the literature, with a significant increase of interest in the topic since the rise of deep learning. However, research in the field suffers from many limitations, including the scarcity of public datasets and code implementations, which hampers the reproducibility and reliability of published results. For this reason, we developed "BarBeR" (Barcode Benchmark Repository), a benchmark designed for testing and comparing barcode detection algorithms. This benchmark includes the code implementation of various detection algorithms for barcodes, along with a suite of useful metrics. It offers a range of test setups and can be expanded to include any localization algorithm. In addition, we provide a large, annotated dataset of 8748 barcode images, combining multiple public barcode datasets with standardized annotation formats for both detection and segmentation tasks. Finally, we share the results obtained from running the benchmark on our dataset, offering valuable insights into the performance of different algorithms.

Segmentation of the Inferior Alveolar Canal (IAC) is a critical aspect of dentistry and maxillofacial imaging, garnering considerable attention in recent research endeavors. Deep learning techniques have shown promising results in this domain, yet their efficacy is still significantly hindered by the limited availability of 3D maxillofacial datasets. An inherent challenge is posed by the size of input volumes, which necessitates a patch-based processing approach that compromises the neural network performance due to the absence of global contextual information. This study introduces a novel approach that harnesses the spatial information within the extracted patches and incorporates it into a Transformer architecture, thereby enhancing the segmentation process through the use of prior knowledge about the patch location. Our method significantly improves the Dice score by a factor of 4 points, with respect to the previous work proposed by Cipriano et al., while also reducing the training steps required by the entire pipeline. By integrating spatial information and leveraging the power of Transformer architectures, this research not only advances the accuracy of IAC segmentation, but also streamlines the training process, offering a promising direction for improving dental and maxillofacial image analysis.

The presence of visible particles in pharmaceutical products is a critical quality issue that demands strict monitoring. Recently, Convolutional Neural Networks (CNNs) have been widely used in industrial settings to detect defects, but there remains a gap in the literature concerning the detection of particles floating in liquid substances, mainly due to the lack of publicly available datasets. In this study, we focus on the detection of foreign particles in pharmaceutical liquid vials, leveraging two state-of-the-art deep-learning approaches adapted to our specific multiclass problem. The first methodology employs a standard ResNet-18 architecture, while the second exploits a Multi-Instance Learning (MIL) technique to efficiently deal with multiple images (sequences) of the same sample. To address the issue of no data availability, we devised and partially released an annotated dataset consisting of sequences containing 19 images for each sample, captured from rotating vials, both with and without impurities. The dataset comprises 2,426 sequences for a total of 46,094 images labeled at the sequence level and including five distinct classes. The proposed methodologies, trained on this new extensive dataset, represent advancements in the field, offering promising strategies to improve the safety and quality control of pharmaceutical products and setting a benchmark for future comparisons.

Introduction. Length of Stay (LOS) and Prolonged Length of Stay (pLOS) are critical indicators of hospital efficiency. Reducing pLOS is crucial for patient safety, autonomy, and bed allocation. This study investigates different machine learning (ML) models to predict LOS and pLOS. Methods. We analyzed a dataset of patients discharged from a northern Italian hospital between 2022 and 2023 as a retrospective cohort study. We compared sixteen regression algorithms and twelve classification methods for predicting LOS as either a continuous or multi-class variable (1-3 days, 4-10 days, >10 days). We also evaluated pLOS prediction using the same models, having pLOS defined as any hospitalization with LOS longer than 8 days. We further analyzed all models using two versions of the same dataset: one containing only structured data (e.g. demographics and clinical information), whereas the second one also containing features extracted from free-text diagnosis. Results. Our results indicate that ensemble models achieved the highest prediction accuracy for both LOS and pLOS, outperforming traditional single-algorithm models, particularly when using both structured and unstructured data extracted from diagnoses. Discussion. The integration of ML, particularly ensemble models, can significantly improve LOS prediction and identify patients at increased risk of pLOS. This information can guide healthcare professionals and bed managers in making informed decisions to enhance patient care and optimize resource allocation.

The Inferior Alveolar Nerve (IAN) is of main interest in the maxillofacial field, as an accurate localization of such nerve reduces the risks of injury during surgical procedures. Although recent literature has focused on developing novel deep learning techniques to produce accurate segmentation masks of the canal containing the IAN, there are still strong limitations due to the scarce amount of publicly available 3D maxillofacial datasets. In this paper, we present an improved version of a previously released tool, IACAT (Inferior Alveolar Canal Annotation Tool), today used by medical experts to produce 3D ground truth annotation. In addition, we release a new dataset, ToothFairy, which is part of the homonymous MICCAI2023 challenge hosted by the Grand-Challenge platform, as an extension of the previously released Maxillo dataset, which was the only publicly available. With ToothFairy, the number of annotations has been increased as well as the quality of existing data.

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.