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The assessment of vascular complexity in the lower limbs provides relevant information about peripheral artery occlusive diseases (PAOD), thus fostering improvements both in therapeutic decisions and prognostic estimation. The current clinical practice consists of visually inspecting and evaluating cine-angiograms of the interested region, which is largely operator-dependent. We present here an automatic method for segmenting the vessel tree and compute a quantitative measure, in terms of fractal dimension (FD), of the vascular complexity. The proposed workflow consists of three main steps: (i) conversion of the cine-angiographies to single static images with a broader field of view, (ii) automatic segmentation of the vascular trees, and (iii) calculation and assessment of FD as complexity index. In particular, this work defines (1) a method to reduce the inter-observer variability in judging vascular complexity in cine-angiography images from patients affected by peripheral artery occlusive disease (PAOD), and (2) the use of Fractal Dimension as a metric of shape complexity of vascular tree. The inter-class correlation coefficient (ICC) is computed as inter-observer agreement metric and to account for possible systematic error, that depends on the experience of the raters. The automatic segmentation of vascular tree achieved an Area Under the Curve mean value of $$0.77~\pm ~0.07$$ 0.77 ± 0.07 , with a min-max range of $$0.57-0.87$$ 0.57 - 0.87 . Absolute operator agreement was higher over the segmented image ($$ICC=0.96$$ I C C = 0.96 ) compared to the video ($$ICC=0.76$$ I C C = 0.76 ) and the a broader field of view image ($$ICC=0.92$$ I C C = 0.92 ). Fractal Dimension computed on both manual segmented images (ground truths) and automatically showed a good correlation with the clinical score (0.85 and 0.75, respectively). Experimental analyses suggest that extracting the vascular tree from cine-angiography can substantially improve the reliability of visual assessment of vascular complexity in PAOD. Results also reveal the effectiveness of FD in evaluating complex vascular tree structures.

Recently, deep learning (DL)-based methods for the generation of synthetic computed tomography (sCT) have received significant research attention as an alternative to classical ones. We present here a systematic review of these methods by grouping them into three categories, according to their clinical applications: I) To replace CT in magnetic resonance (MR)-based treatment planning. II) Facilitate cone-beam computed tomography (CBCT)-based image-guided adaptive radiotherapy. III) Derive attenuation maps for the correction of positron emission tomography (PET). Appropriate database searching was performed on journal articles published between January 2014 and December 2020. The DL methods' key characteristics were extracted from each eligible study, and a comprehensive comparison among network architectures and metrics was reported. A detailed review of each category was given, highlighting essential contributions, identifying specific challenges, and summarising the achievements. Lastly, the statistics of all the cited works from various aspects were analysed, revealing the popularity and future trends, and the potential of DL-based sCT generation. The current status of DL-based sCT generation was evaluated, assessing the clinical readiness of the presented methods., 59 pages, 4 figures, 5 tables; two first equally contributing authors; Medical Physics. 2021

The coronavirus disease 19 (COVID-19) pandemic is having a dramatic impact on society and healthcare systems. In this complex scenario, lung computerized tomography (CT) may play an important prognostic role. However, datasets released so far present limitations that hamper the development of tools for quantitative analysis. In this paper, we present an open-source lung CT dataset comprising information on 50 COVID-19-positive patients. The CT volumes are provided along with (i) an automatic threshold-based annotation obtained with a Gaussian mixture model (GMM) and (ii) a scoring provided by an expert radiologist. This score was found to significantly correlate with the presence of ground glass opacities and the consolidation found with GMM. The dataset is freely available in an ITK-based file format under the CC BY-NC 4.0 license. The code for GMM fitting is publicly available, as well. We believe that our dataset will provide a unique opportunity for researchers working in the field of medical image analysis, and hope that its release will lay the foundations for the successfully implementation of algorithms to support clinicians in facing the COVID-19 pandemic.

Purpose Automated delineation of structures and organs is a key step in medical imaging. However, due to the large number and diversity of structures and the large variety of segmentation algorithms, a consensus is lacking as to which automated segmentation method works best for certain applications. Segmentation challenges are a good approach for unbiased evaluation and comparison of segmentation algorithms. Methods In this work, we describe and present the results of the Head and Neck Auto-Segmentation Challenge 2015, a satellite event at the Medical Image Computing and Computer Assisted Interventions (MICCAI) 2015 conference. Six teams participated in a challenge to segment nine structures in the head and neck region of CT images: brainstem, mandible, chiasm, bilateral optic nerves, bilateral parotid glands, and bilateral submandibular glands. Results This paper presents the quantitative results of this challenge using multiple established error metrics and a well-defined ranking system. The strengths and weaknesses of the different auto-segmentation approaches are analyzed and discussed. Conclusions The Head and Neck Auto-Segmentation Challenge 2015 was a good opportunity to assess the current state-of-the-art in segmentation of organs at risk for radiotherapy treatment. Participating teams had the possibility to compare their approaches to other methods under unbiased and standardized circumstances. The results demonstrate a clear tendency toward more general purpose and fewer structure-specific segmentation algorithms.

Purpose: Multiatlas based segmentation is largely used in many clinical and research applications. Due to its good performances, it has recently been included in some commercial platforms for radiotherapy planning and surgery guidance. Anyway, to date, a software with no restrictions about the anatomical district and image modality is still missing. In this paper we introduce plastimatch mabs, an open source software that can be used with any image modality for automatic segmentation. Methods: plastimatch mabs workflow consists of two main parts: (1) an offline phase, where optimal registration and voting parameters are tuned and (2) an online phase, where a new patient is labeled from scratch by using the same parameters as identified in the former phase. Several registration strategies, as well as different voting criteria can be selected. A flexible atlas selection scheme is also available. To prove the effectiveness of the proposed software across anatomical districts and image modalities, it was tested on two very different scenarios: head and neck (H&N) CT segmentation for radiotherapy application, and magnetic resonance image brain labeling for neuroscience investigation. Results: For the neurological study, minimum dice was equal to 0.76 (investigated structures: left and right caudate, putamen, thalamus, and hippocampus). For head and neck case, minimum dice was 0.42 for the most challenging structures (optic nerves and submandibular glands) and 0.62 for the other ones (mandible, brainstem, and parotid glands). Time required to obtain the labels was compatible with a real clinical workflow (35 and 120 min). Conclusions: The proposed software fills a gap in the multiatlas based segmentation field, since all currently available tools (both for commercial and for research purposes) are restricted to a well specified application. Furthermore, it can be adopted as a platform for exploring MABS parameters and as a reference implementation for comparing against other segmentation algorithms.

In radiation therapy, a key step for a successful cancer treatment is image-based treatment planning. One objective of the planning phase is the fast and accurate segmentation of organs at risk and target structures from medical images. However, manual delineation of organs, which is still the gold standard in many clinical environments, is time-consuming and prone to inter-observer variations. Consequently, many automated segmentation methods have been developed. In this work, we train two hierarchical 3D neural networks to segment multiple organs at risk in the head and neck area. First, we train a coarse network on size-reduced medical images to locate the organs of interest. Second, a subsequent fine network on full-resolution images is trained for a final accurate segmentation. The proposed method is purely deep learning based; accordingly, no pre-registration or post-processing is required. The approach has been applied on a publicly available computed tomography dataset, created for the MICCAI 2015 Auto-Segmentation challenge. In an extensive evaluation process, the best configurations for the trained networks have been determined. Compared to the existing methods, the presented approach shows state-of-the-art performance for the segmentation of seven different structures in the head and neck area. We conclude that 3D neural networks outperform the most existing model- and atlas-based methods for the segmentation of organs at risk in the head and neck area. The ease of use, high accuracy and the test time efficiency of the method make it promising for image-based treatment planning in clinical practice.

The assessment of vascular complexity in the lower limbs provides important information about peripheral artery diseases, with a relevant impact on both therapeutic decisions and on prognostic estimation. Currently, the evaluation is carried out by visual inspection of cine-angiograms, which is largely operator-dependent. An automatic image analysis could offer a fast and more reliable technique to support physicians with the clinical management of these patients. In this work, we introduce a new method to automatically segment the vascular tree from cine-angiography images, in order to improve the clinical interpretation of the complexity of vascular collaterals in Peripheral Arterial Occlusive Disease (PAOD) patients. The approach is based on: (1) a feature-detection method to convert the video into a static image with lager Field Of View (FOV) and (2) a custom Convolutional Neural Network (CNN) for the segmentation of vascular structure. Experimental evaluations over a set of clinical cases confirm the viability of the approach: accuracy is assessed in terms of area under the ROC curve, where an average value of \(0.988 \pm 0.006\) is measured.

Multi atlas based segmentation (MABS) uses a database of atlas images, and an atlas selection process is used to choose an atlas subset for registration and voting. In the current state of the art, atlases are chosen according to a similarity criterion between the target subject and each atlas in the database. In this paper, we propose a new concept for atlas selection that relies on selecting the best performing group of atlases rather than the group of highest scoring individual atlases. Experiments were performed using CT images of 50 patients, with contours of brainstem and parotid glands. The dataset was randomly split into two groups: 20 volumes were used as an atlas database and 30 served as target subjects for testing. Classic oracle selection, where atlases are chosen by the highest dice similarity coefficient (DSC) with the target, was performed. This was compared to oracle group selection, where all the combinations of atlas subgroups were considered and scored by computing DSC with the target subject. Subsequently, convolutional neural networks were designed to predict the best group of atlases. The results were also compared with the selection strategy based on normalized mutual information (NMI). Oracle group was proven to be significantly better than classic oracle selection (p

During the last years Deep Learning and especially Convolutional Neural Networks (CNN) have set new standards for different computer vision tasks like image classification and semantic segmentation. In this paper, a CNN for 3D volume segmentation based on recently introduced deep learning components will be presented. In addition to using image patches as input for a CNN, the usage of orthogonal patches, which combine shape and locality information with intensity information for CNN training will be evaluated. For this purpose a publically available CT dataset of the head-neck region has been used and the results have been compared with other state-of-the art atlas- and model-based segmentation approaches.

Purpose: In this work we present the validation of Plastimatch MABS, an open source software for multi atlas based segmentation of medical images. Methods: The validation was performed on two different clinical datasets: 1) 25 CT image volumes of patients treated for HN 2) 20 MRI series of patients having a neurological diagnosis. For the first set, 8 organs at risk to be spared during the therapy were segmented: mandible, brainstem and both left/right optic nerves, parotid and submandibular glands. For the neurological set, 4 paired brain structures were segmented: left/right caudate, putamen, thalamus and hippocampus. For both cases a leave one out approach was used for validation. Tests were performed on common hardware (Intel Xeon CPU @ 2.5 GHz, 8 GB of RAM). Results: Segmentation accuracy was quantified in terms of Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff Distance (HD). The average DSCs computed over all the labels were 0.70 for H&N application and 0.85 for neuroscience case. HD values confirm the DSCs (4.92 mm and 1.41 mm). Average segmentation time for the two applications was 35 and 120 mins respectively. Conclusion: Plastimatch MABS has been validated for multi atlas based segmentation of medical images. Accuracy was demonstrated for different anatomical districts and image modalities. The results show that Plastimatch MABS can be used for a wide range of applications. The presented approach can provide fully automatic and robust segmentation and is also compatible with timing requirements for clinical applications.

Purpose: Accurate delineation of organs at risk (OARs) is a precondition for intensity modulated radiation therapy. However, manual delineation of OARs is time consuming and prone to high interobserver variability. Because of image artifacts and low image contrast between different structures, however, the number of available approaches for autosegmentation of structures in the head-neck area is still rather low. In this project, a new approach for automated segmentation of head-neck CT images that combine the robustness of multiatlas-based segmentation with the flexibility of geodesic active contours and the prior knowledge provided by statistical appearance models is presented. Methods: The presented approach is using an atlas-based segmentation approach in combination with label fusion in order to initialize a segmentation pipeline that is based on using statistical appearance models and geodesic active contours. An anatomically correct approximation of the segmentation result provided by atlas-based segmentation acts as a starting point for an iterative refinement of this approximation. The final segmentation result is based on using model to image registration and geodesic active contours, which are mutually influencing each other. Results: 18 CT images in combination with manually segmented labels of parotid glands and brainstem were used in a leave-one-out cross validation scheme in order to evaluate the presented approach. For this purpose, 50 different statistical appearance models have been created and used for segmentation. Dice coefficient (DC), mean absolute distance and max. Hausdorff distance between the autosegmentation results and expert segmentations were calculated. An average Dice coefficient of DC = 0.81 (right parotid gland), DC = 0.84 (left parotid gland), and DC = 0.86 (brainstem) could be achieved. Conclusions: The presented framework provides accurate segmentation results for three important structures in the head neck area. Compared to a segmentation approach based on using multiple atlases in combination with label fusion, the proposed hybrid approach provided more accurate results within a clinically acceptable amount of time.