ASOCEM: Automatic Segmentation Of Contaminations in cryo-EM.

Particle picking is currently a critical step in the cryo-electron microscopy single particle reconstruction pipeline. Contaminations in the acquired micrographs severely degrade the performance of particle pickers, resulting in many "non-particles" in the collected stack of particles. In this paper, we present ASOCEM (Automatic Segmentation Of Contaminations in cryo-EM), an automatic method to detect and segment contaminations, which requires as an input only the approximate particle size. In particular, it does not require any parameter tuning nor manual intervention. Our method is based on the observation that the statistical distribution of contaminated regions is different from that of the rest of the micrograph. This nonrestrictive assumption allows to automatically detect various types of contaminations, from the carbon edges of the supporting grid to high contrast blobs of different sizes. We demonstrate the efficiency of our algorithm using various experimental data sets containing various types of contaminations. ASOCEM is integrated as part of the KLT picker (Eldar et al., 2020) and is available at https://github.com/ShkolniskyLab/kltpicker2.

Segmentation of Plantar Foot Thermal Images Using Prior Information.

Diabetic foot (DF) complications are associated with temperature variations. The occurrence of DF ulceration could be reduced by using a contactless thermal camera. The aim of our study is to provide a decision support tool for the prevention of DF ulcers. Thus, the segmentation of the plantar foot in thermal images is a challenging step for a non-constraining acquisition protocol. This paper presents a new segmentation method for plantar foot thermal images. This method is designed to include five pieces of prior information regarding the aforementioned images. First, a new energy term is added to the snake of Kass et al. in order to force its curvature to match that of the prior shape, which has a known form. Second, we defined the initial contour as the downsized prior-shape contour, which is placed inside the plantar foot surface in a vertical orientation. This choice makes the snake avoid strong false boundaries present outside the plantar region when evolving. As a result, the snake produces a smooth contour that rapidly converges to the true boundaries of the foot. The proposed method is compared to two classical prior-shape snake methods, that of Ahmed et al. and that of Chen et al. A database of 50 plantar foot thermal images was processed. The results show that the proposed method outperforms the previous two methods with a root-mean-square error of 5.12 pixels and a dice similarity coefficient of 94%. The segmentation of the plantar foot regions in the thermal images helped us to assess the point-to-point temperature differences between the two feet in order to detect hyperthermia regions. The presence of such regions is the pre-sign of ulcers in the diabetic foot. Furthermore, our method was applied to hyperthermia detection to illustrate the promising potential of thermography in the case of the diabetic foot. Associated with a friendly acquisition protocol, the proposed segmentation method is the first step for a future mobile smartphone-based plantar foot thermal analysis for diabetic foot patients.

Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours.

BACKGROUND: Segmentation of the left atrium (LA) is required to evaluate atrial size and function, which are important imaging biomarkers for a wide range of cardiovascular conditions, such as atrial fibrillation, stroke, and diastolic dysfunction. LA segmentations are currently being performed manually, which is time-consuming and observer-dependent. METHODS: This study presents an automated image processing algorithm for time-resolved LA segmentation in cardiac magnetic resonance imaging (MRI) long-axis cine images of the 2-chamber (2ch) and 4-chamber (4ch) views using active contours. The proposed algorithm combines mitral valve tracking, automated threshold calculation, edge detection on a radially resampled image, edge tracking based on Dijkstra's algorithm, and post-processing involving smoothing and interpolation. The algorithm was evaluated in 37 patients diagnosed mainly with paroxysmal atrial fibrillation. Segmentation accuracy was assessed using the Dice similarity coefficient (DSC) and Hausdorff distance (HD), with manual segmentations in all time frames as the reference standard. For inter-observer variability analysis, a second observer performed manual segmentations at end-diastole and end-systole on all subjects. RESULTS: The proposed automated method achieved high performance in segmenting the LA in long-axis cine sequences, with a DSC of 0.96 for 2ch and 0.95 for 4ch, and an HD of 5.5 mm for 2ch and 6.4 mm for 4ch. The manual inter-observer variability analysis had an average DSC of 0.95 and an average HD of 4.9 mm. CONCLUSION: The proposed automated method achieved performance on par with human experts analyzing MRI images for evaluation of atrial size and function. Video Abstract.

Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging.

Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.

Optimising adjacent membrane segmentation and parameterisation in multicellular aggregates by piecewise active contours.

In fluorescence microscopy imaging, the segmentation of adjacent cell membranes within cell aggregates, multicellular samples, tissue, organs, or whole organisms remains a challenging task. The lipid bilayer is a very thin membrane when compared to the wavelength of photons in the visual spectra. Fluorescent molecules or proteins used for labelling membranes provide a limited signal intensity, and light scattering in combination with sample dynamics during in vivo imaging lead to poor or ambivalent signal patterns that hinder precise localisation of the membrane sheets. In the proximity of cells, membranes approach and distance each other. Here, the presence of membrane protrusions such as blebs; filopodia and lamellipodia; microvilli; or membrane vesicle trafficking, lead to a plurality of signal patterns, and the accurate localisation of two adjacent membranes becomes difficult. Several computational methods for membrane segmentation have been introduced. However, few of them specifically consider the accurate detection of adjacent membranes. In this article we present ALPACA (ALgorithm for Piecewise Adjacent Contour Adjustment), a novel method based on 2D piecewise parametric active contours that allows: (i) a definition of proximity for adjacent contours, (ii) a precise detection of adjacent, nonadjacent, and overlapping contour sections, (iii) the definition of a polyline for an optimised shared contour within adjacent sections and (iv) a solution for connecting adjacent and nonadjacent sections under the constraint of preserving the inherent cell morphology. We show that ALPACA leads to a precise quantification of adjacent and nonadjacent membrane zones in regular hexagons and live image sequences of cells of the parapineal organ during zebrafish embryo development. The algorithm detects and corrects adjacent, nonadjacent, and overlapping contour sections within a selected adjacency distance d, calculates shared contour sections for neighbouring cells with minimum alterations of the contour characteristics, and presents piecewise active contour solutions, preserving the contour shape and the overall cell morphology. ALPACA quantifies adjacent contours and can improve the meshing of 3D surfaces, the determination of forces, or tracking of contours in combination with previously published algorithms. We discuss pitfalls, strengths, and limits of our approach, and present a guideline to take the best decision for varying experimental conditions for in vivo microscopy.

A Novel Method of Aircraft Detection Based on High-Resolution Panchromatic Optical Remote Sensing Images.

In target detection of optical remote sensing images, two main obstacles for aircraft target detection are how to extract the candidates in complex gray-scale-multi background and how to confirm the targets in case the target shapes are deformed, irregular or asymmetric, such as that caused by natural conditions (low signal-to-noise ratio, illumination condition or swaying photographing) and occlusion by surrounding objects (boarding bridge, equipment). To solve these issues, an improved active contours algorithm, namely region-scalable fitting energy based threshold (TRSF), and a corner-convex hull based segmentation algorithm (CCHS) are proposed in this paper. Firstly, the maximal variance between-cluster algorithm (Otsu's algorithm) and region-scalable fitting energy (RSF) algorithm are combined to solve the difficulty of targets extraction in complex and gray-scale-multi backgrounds. Secondly, based on inherent shapes and prominent corners, aircrafts are divided into five fragments by utilizing convex hulls and Harris corner points. Furthermore, a series of new structure features, which describe the proportion of targets part in the fragment to the whole fragment and the proportion of fragment to the whole hull, are identified to judge whether the targets are true or not. Experimental results show that TRSF algorithm could improve extraction accuracy in complex background, and that it is faster than some traditional active contours algorithms. The CCHS is effective to suppress the detection difficulties caused by the irregular shape.

Tool for Semiautomatic Labeling of Moving Objects in Video Sequences: TSLAB.

An advanced and user-friendly tool for fast labeling of moving objects captured with surveillance sensors is proposed, which is available to the public. This tool allows the creation of three kinds of labels: moving objects, shadows and occlusions. These labels are created at both the pixel level and object level, which makes them suitable to assess the quality of both moving object detection strategies and tracking algorithms. The labeling can be performed easily and quickly thanks to a very friendly graphical user interface that allows one to automatize many common operations. This interface also includes some semiautomatic advanced tools that simplify the labeling tasks and drastically reduce the time required to obtain high-quality results.

Computer aided weld defect delineation using statistical parametric active contours in radiographic inspection.

A perfect knowledge of a defect shape is determinant for the analysis step in automatic radiographic inspection. Image segmentation is carried out on radiographic images and extract defects indications. This paper deals with weld defect delineation in radiographic images. The proposed method is based on a new statistics-based explicit active contour. An association of local and global modeling of the image pixels intensities is used to push the model to the desired boundaries. Furthermore, other strategies are proposed to accelerate its evolution and make the convergence speed depending only on the defect size as selecting a band around the active contour curve. The experimental results are very promising, since experiments on synthetic and radiographic images show the ability of the proposed model to extract a piece-wise homogenous object from very inhomogeneous background, even in a bad quality image.

Segmentation and Volumetric Analysis of Heart from Cardiac CT Images.

PURPOSE: Cardiac CT is a valuable diagnostic tool in evaluating cardiovascular diseases. Accurate segmentation of the heart and its structures from cardiac CT and MRI images is essential for diagnosing functional abnormalities, treatment plans and cardiovascular diseases management. Accurate segmentation and quantitative assessments are still a challenge. Manual delineation of the heart from the scan images is labour-intensive, time-consuming, and error prone as it depends on the radiologist's experience. Thus, automated techniques are highly desirable as they can significantly improve the efficiency and accuracy of image analysis. METHOD: This work addresses the above problems. A new, image-driven, fast, and fully automatic segmentation method was developed to segment the heart from CT images using a processing pipeline of adaptive median filter, multi-level thresholding, active contours, mathematical morphology, and the knowledge of human anatomy to delineate the regions of interest. RESULTS: The algorithm proposed is simple to implement and validate and requires no human intervention. The method is tested on the 'Image CHD' DICOM images (multi-centre, clinically approved single-phase de-identified images), and the results obtained were validated against the ground truths provided with the dataset. The results show an average Dice score, Jaccard score, and Hausdorff distance of 0.866, 0.776, and 33.29 mm, respectively, for the segmentation of the heart's chambers, aorta, and blood vessels. The results and the ground truths were compared using Bland-Altmon plots. CONCLUSION: The heart was correctly segmented from the CT images using the proposed method. Further this segmentation technique can be used to develop AI based solutions for segmentation.

Automatic classification and segmentation of blast cells using deep transfer learning and active contours.

INTRODUCTION: Acute lymphoblastic leukemia (ALL) presents a formidable challenge in hematological malignancies, necessitating swift and precise diagnostic techniques for effective intervention. The conventional manual microscopy of blood smears, although widely practiced, suffers from significant limitations including labor-intensity and susceptibility to human error, particularly in distinguishing the subtle differences between normal and leukemic cells. METHODS: To overcome these limitations, our research introduces the ALLDet classifier, an innovative tool employing deep transfer learning for the automated analysis and categorization of ALL from White Blood Cell (WBC) nuclei images. Our investigation encompassed the evaluation of nine state-of-the-art pre-trained convolutional neural network (CNN) models, namely VGG16, VGG19, ResNet50, ResNet101, DenseNet121, DenseNet201, Xception, MobileNet, and EfficientNetB3. We augmented this approach by incorporating a sophisticated contour-based segmentation technique, derived from the Chan-Vese model, aimed at the meticulous segmentation of blast cell nuclei in blood smear images, thereby enhancing the accuracy of our analysis. RESULTS: The empirical assessment of these methodologies underscored the superior performance of the EfficientNetB3 model, which demonstrated exceptional metrics: a recall specificity of 98.5%, precision of 95.86%, F1-score of 97.16%, and an overall accuracy rate of 97.13%. The Chan-Vese model's adaptability to the irregular shapes of blast cells and its noise-resistant segmentation capability were key to capturing the complex morphological changes essential for accurate segmentation. CONCLUSION: The combined application of the ALLDet classifier, powered by EfficientNetB3, with our advanced segmentation approach, emerges as a formidable advancement in the early detection and accurate diagnosis of ALL. This breakthrough not only signifies a pivotal leap in leukemia diagnostic methodologies but also holds the promise of significantly elevating the standards of patient care through the provision of timely and precise diagnoses. The implications of this study extend beyond immediate clinical utility, paving the way for future research to further refine and enhance the capabilities of artificial intelligence in medical diagnostics.

Foveal avascular zone segmentation using deep learning-driven image-level optimization and fundus photographs.

The foveal avascular zone (FAZ) is a retinal area devoid of capillaries and associated with multiple retinal pathologies and visual acuity. Optical Coherence Tomography Angiography (OCT-A) is a very effective means of visualizing retinal vascular and avascular areas, but its use remains limited to research settings due to its complex optics limiting availability. On the other hand, fundus photography is widely available and often adopted in population studies. In this work, we test the feasibility of estimating the FAZ from fundus photos using three different approaches. The first two approaches rely on pixel-level and image-level FAZ information to segment FAZ pixels and regress FAZ area, respectively. The third is a training mask-free pipeline combining saliency maps with an active contours approach to segment FAZ pixels while being trained on image-level measures of the FAZ areas. This enables training FAZ segmentation methods without manual alignment of fundus and OCT-A images, a time-consuming process, which limits the dataset that can be used for training. Segmentation methods trained on pixel-level labels and image-level labels had good agreement with masks from a human grader (respectively DICE of 0.45 and 0.4). Results indicate the feasibility of using fundus images as a proxy to estimate the FAZ when angiography data is not available.

Semi-supervised COVID-19 volumetric pulmonary lesion estimation on CT images using probabilistic active contour and CNN segmentation.

Purpose: A semi-supervised two-step methodology is proposed to obtain a volumetric estimation of COVID-19-related lesions on Computed Tomography (CT) images. Methods: First, damaged tissue was segmented from CT images using a probabilistic active contours approach. Second, lung parenchyma was extracted using a previously trained U-Net. Finally, volumetric estimation of COVID-19 lesions was calculated considering the lung parenchyma masks.Our approach was validated using a publicly available dataset containing 20 CT COVID-19 images previously labeled and manually segmented. Then, it was applied to 295 COVID-19 patients CT scans admitted to an intensive care unit. We compared the lesion estimation between deceased and survived patients for high and low-resolution images. Results: A comparable median Dice similarity coefficient of 0.66 for the 20 validation images was achieved. For the 295 images dataset, results show a significant difference in lesion percentages between deceased and survived patients, with a p-value of 9.1 × 10-4 in low-resolution and 5.1 × 10-5 in high-resolution images. Furthermore, the difference in lesion percentages between high and low-resolution images was 10 % on average. Conclusion: The proposed approach could help estimate the lesion size caused by COVID-19 in CT images and may be considered an alternative to getting a volumetric segmentation for this novel disease without the requirement of large amounts of COVID-19 labeled data to train an artificial intelligence algorithm. The low variation between the estimated percentage of lesions in high and low-resolution CT images suggests that the proposed approach is robust, and it may provide valuable information to differentiate between survived and deceased patients.

3D carotid artery segmentation using shape-constrained active contours.

Reconstruction of the carotid artery is demanded in the detection and characterization of atherosclerosis. This study proposes a shape-constrained active contour model for segmenting the carotid artery from MR images, which embeds the output of the deep learning network into the active contour. First the centerline of the carotid artery is localized and then modified active contour initialized from the centerline is used to extract the vessel lumen, finally the probability atlas generated by the deep learning network in polar representation domain is integrated into the active contour as a prior information to detect the outer wall. The results showed that the proposed active contour model was efficient and comparable to manual segmentation.

Attractive deep morphology-aware active contour network for vertebral body contour extraction with extensions to heterogeneous and semi-supervised scenarios.

Automatic vertebral body contour extraction (AVBCE) from heterogeneous spinal MRI is indispensable for the comprehensive diagnosis and treatment of spinal diseases. However, AVBCE is challenging due to data heterogeneity, image characteristics complexity, and vertebral body morphology variations, which may cause morphology errors in semantic segmentation. Deep active contour-based (deep ACM-based) methods provide a promising complement for tackling morphology errors by directly parameterizing the contour coordinates. Extending the target contours' capture range and providing morphology-aware parameter maps are crucial for deep ACM-based methods. For this purpose, we propose a novel Attractive Deep Morphology-aware actIve contouR nEtwork (ADMIRE) that embeds an elaborated contour attraction term (CAT) and a comprehensive contour quality (CCQ) loss into the deep ACM-based framework. The CAT adaptively extends the target contours' capture range by designing an all-to-all force field to enable the target contours' energy to contribute to farther locations. Furthermore, the CCQ loss is carefully designed to generate morphology-aware active contour parameters by simultaneously supervising the contour shape, tension, and smoothness. These designs, in cooperation with the deep ACM-based framework, enable robustness to data heterogeneity, image characteristics complexity, and target contour morphology variations. Furthermore, the deep ACM-based ADMIRE is able to cooperate well with semi-supervised strategies such as mean teacher, which enables its function in semi-supervised scenarios. ADMIRE is trained and evaluated on four challenging datasets, including three spinal datasets with more than 1000 heterogeneous images and more than 10000 vertebrae bodies, as well as a cardiac dataset with both normal and pathological cases. Results show ADMIRE achieves state-of-the-art performance on all datasets, which proves ADMIRE's accuracy, robustness, and generalization ability.

Discrete light sheet microscopic segmentation of left ventricle using morphological tuning and active contours.

Left ventricular segmentation using cardiovascular MR scan is required for the diagnosis and further cure of cardiac diseases. Automatic systems for left ventricle segmentation are being studied for attaining more accurate results in a shorter period of time. A novel algorithm introducing discrete segmentation of left ventricle achieves an independent processing of images swiftly. The workflow consists of four segments; first, automated localization is performed on the MR image. Second, performing preprocessing intimately improves and enhances the quality of image using mean contrast adjustment. Central segmentation of endocardium and epicardium layers includes novel MTAC (Morphological tuning using active contours) segmentation algorithm that provides a perfect combination of active contours and morphological tuning to bring an adequate and desirable segmentation. The prospective snake model is a restrained progression, which takes iterations for an impulse throughout the left ventricle contours. At the end, contrast based refining overcomes minor edge problems for both outer and inner boundaries. Proposed algorithm is evaluated via Sunnybrook cardiac MR images by producing an overall average perpendicular distance 2.45 mm, an average dice matrix (endo: 91.3%; epi: 92.16%) and 91.7% dice matrix of overall endocardium and epicardium contours from ground truth contours.

A multiphase texture-based model of active contours assisted by a convolutional neural network for automatic CT and MRI heart ventricle segmentation.

BACKGROUND: Left and right ventricle automatic segmentation remains one of the more important tasks in computed aided diagnosis. Active contours have shown to be efficient for this task, however they often require user interaction to provide the initial position, which drives the tool substantially dependent on a prior knowledge and a manual process. METHODS: We propose to overcome this limitation with a Convolutional Neural Network (CNN) to reach the assumed target locations. This is followed by a novel multiphase active contour method based on texture that enhances whole heart patterns leading to an accurate identification of distinct regions, mainly left (LV) and right ventricle (RV) for the purposes of this work. RESULTS: Experiments reveal that the initial location and estimated shape provided by the CNN are of great concern for the subsequent active contour stage. We assessed our method on two short data sets with Dice scores of 93% (LV-CT), 91% (LV-MRI), 0.86% (RV-CT) and 0.85% (RV-MRI). CONCLUSION: Our approach overcomes the performance of other techniques by means of a multiregion segmentation assisted by a CNN trained with a limited data set, a typical issue in medical imaging.

Semi-automatic vessel detection for challenging cases of peripheral arterial disease.

OBJECTIVES: Manual or semi-automated segmentation of the lower extremity arterial tree in patients with Peripheral arterial disease (PAD) remains a notoriously difficult and time-consuming task. The complex manifestations of the disease, including discontinuities of the vascular flow channels, the presence of calcified atherosclerotic plaque in close vicinity to adjacent bone, and the presence of metal or other imaging artifacts currently preclude fully automated vessel identification. New machine learning techniques may alleviate this challenge, but require large and reasonably well segmented training data. METHODS: We propose a novel semi-automatic vessel tracking approach for peripheral arteries to facilitate and accelerate the creation of annotated training data by expert cardiovascular radiologists or technologists, while limiting the number of necessary manual interactions, and reducing processing time. After automatically classifying blood vessels, bones, and other tissue, the relevant vessels are tracked and organized in a tree-like structure for further visualization. RESULTS: We conducted a pilot (N = 9) and a clinical study (N = 24) in which we assess the accuracy and required time for our approach to achieve sufficient quality for clinical application, with our current clinically established workflow as the standard of reference. Our approach enabled expert physicians to readily identify all clinically relevant lower extremity arteries, even in problematic cases, with an average sensitivity of 92.9%, and an average specificity and overall accuracy of 99.9%. CONCLUSIONS: Compared to the clinical workflow in our collaborating hospitals (28:40 ± 7:45 [mm:ss]), our approach (17:24 ± 6:44 [mm:ss]) is on average 11:16 [mm:ss] (39%) faster.

Automatic segmentation and tracking of biological prosthetic heart valves.

Purpose: Prosthetic heart valve designs must be rigorously tested using cardiovascular equipment. The valve orifice area over time constitutes a key quality metric which is typically assessed manually, thus a tedious and error-prone task. From a computer vision viewpoint, a major unsolved issue lies in the orifice being partly occluded by the leaflets' inner side or inaccurately depicted due to its transparency. Here, we address this issue, which allows us to focus on the accurate and automatic computation of valve orifice areas. Approach: We propose a segmentation approach based on the detection of the leaflets' free edges. Using video frames recorded with a high-speed digital camera during in vitro simulations, an initial estimation of the orifice area is first obtained via active contouring and thresholding and then refined to capture the leaflet free edges via a curve transformation mechanism. Results: Experiments on video data from pulsatile flow testing demonstrate the effectiveness of our approach: a root-mean-square error (RMSE) on the temporal extracted orifice areas between 0.8% and 1.2%, an average Jaccard similarity coefficient between 0.933 and 0.956, and an average Hausdorff distance between 7.2 and 11.9 pixels. Conclusions: Our approach significantly outperformed a state-of-the-art algorithm in terms of evaluation metrics related to valve design (RMSE) and computer vision (accuracy of the orifice shape). It can also cope with lower quality videos and is better at processing frames showing an almost closed valve, a crucial quality for assessing valve design malfunctions related to their improper closing.

Morphological active contour model for automatic brain tumor extraction from multimodal magnetic resonance images.

BACKGROUND: Brain tumor extraction from magnetic resonance (MR) images is challenging due to variations in the location, shape, size and intensity of tumors. Manual delineation of brain tumors from MR images is time-consuming and prone to human errors. METHOD: In this paper, we present a method for automatic tumor extraction from multimodal MR images. Brain tumors are first detected using k-means clustering. A morphological region-based active contour model is then used for tumor extraction using an initial contour defined based on the boundary of the detected brain tumor regions. The contour evolution for tumor extraction was performed using successive application of morphological operators. In our model, a Gaussian distribution was used to model local image intensities. The spatial correlation between neighboring voxels was also modeled using Markov random field. RESULTS: The proposed method was evaluated on BraTS 2013 dataset including patients with high-grade and low-grade tumors. In comparison with other active contour based methods, the proposed method yielded better performance on tumor segmentation with mean Dice similarity coefficients of 0.9179 ( ±â€¯0.025) and 0.8910 ( ±â€¯0.042) obtained on high-grade and low-grade tumors, respectively. CONCLUSION: The proposed method achieved higher accuracies for brain tumor extraction in comparison to other contour-based methods.

Automated segmentation of fractured distal radii by 3D geodesic active contouring of in vivo HR-pQCT images.

Radius fractures are among the most common fracture types; however, there is limited consensus on the standard of care. A better understanding of the fracture healing process could help to shape future treatment protocols and thus improve functional outcomes of patients. High-resolution peripheral quantitative computed tomography (HR-pQCT) allows monitoring and evaluation of the radius on the micro-structural level, which is crucial to our understanding of fracture healing. However, current radius fracture studies using HR-pQCT are limited by the lack of automated contouring routines, hence only including small number of patients due to the prohibitively time-consuming task of manually contouring HR-pQCT images. In the present study, a new method to automatically contour images of distal radius fractures based on 3D morphological geodesic active contours (3D-GAC) is presented. Contours of 60 HR-pQCT images of fractured and conservatively treated radii spanning the healing process up to one year post-fracture are compared to the current gold standard, hand-drawn 2D contours, to assess the accuracy of the algorithm. Furthermore, robustness was established by applying the algorithm to HR-pQCT images of intact radii of 73 patients and comparing the resulting morphometric indices to the gold standard patient evaluation including a threshold- and dilation-based contouring approach. Reproducibility was evaluated using repeat scans of intact radii of 19 patients. The new 3D-GAC approach offers contours within inter-operator variability for images of fractured distal radii (mean Dice score of 0.992 ± 0.005 versus median operator Dice score of 0.992 ± 0.006). The generated contours for images of intact radii yielded morphometric indices within the in vivo reproducibility limits compared to the current gold standard. Additionally, the 3D-GAC approach shows an improved robustness against failure (n = 5) when dealing with cortical interruptions, fracture fragments, etc. compared with the automatic, default manufacturer pipeline (n = 40). Using the 3D-GAC approach assures consistent results, while reducing the need for time-consuming hand-contouring.