Fast and accurate 3-D spine MRI segmentation using FastCleverSeg

Accurate and efficient segmenting of vertebral bodies, muscles, and discs is crucial for analyzing various spinal diseases. However, traditional methods are either laborious and time-consuming (manual segmentation) or require extensive training data (fully automatic segmentation). FastCleverSeg, our proposed semi-automatic segmentation approach, addresses those limitations by significantly reducing user interaction while maintaining high accuracy. First, we reduce user interaction by requiring the manual annotation of only two or three slices. Next, we automatically Estimate the Annotation on Intermediary Slices (EANIS) using traditional computer vision/graphics concepts. Finally, our proposed method leverages improved voxel weight balancing to achieve fast and precise volumetric segmentation in the segmentation process. Experimental evaluations on our assembled diverse MRI databases comprising 179 patients (60 male, 119 female), demonstrate a remarkable 25 ms (30 ms standard deviation) processing time and a significant reduction in user interaction compared to existing approaches. Importantly, FastCleverSeg maintains or surpasses the segmentation quality of competing methods, achieving a Dice score of 94%. This invaluable tool empowers physicians to efficiently generate reliable ground truths, expediting the segmentation process and paving the way for future integration with deep learning approaches. In turn, this opens exciting possibilities for future fully automated spine segmentation.

Imaging finding and analysis of brain lymphoma in contrast-enhanced fluid attenuated inversion recovery sequence

OBJECTIVE

The purpose of this retrospective study was to report and analyze the image findings of contrast-enhanced fluid-attenuated inversion recovery (CE-FLAIR) sequence of lymphoma in the brain.

MATERIAL AND METHODS

Thirty-two immunocompetent patients with biopsy-proven diffuse large B-cell type lymphoma in the brain were evaluated with pre-treatment MRI examinations from August 2014 to April 2020. As stereotactic studies on the day of biopsy, FLAIR and T1-weighted axial images were acquired in 2 mm thickness, before and after administrating gadolinium-based contrast agents, with 3.0 Tesla MR machines. Respective subtraction images were also obtained for both CE-FLAIR and contrast-enhanced T1-wieghted image (CE-T1WI) sequences. The imaging findings, especially the enhancement pattern on CE-FLAIR sequence, were analyzed qualitatively and quantitatively, using semi-automatic segmentation.

RESULTS

On CE-FLAIR images, brain lymphomas were poorly enhanced, while showing peripheral rim enhancement (54 of 58 lesions, 93.1 %) and central enhancing foci (40 of 58 lesions, 69.0 %). Seventy percent of central enhancing foci were correlated to areas with low signal intensity on CE-T1WI. In quantitative analysis, the mean signal intensity of CE-T1WI subtraction was 490.44 and that of FLAIR subtraction was 206.13. The standard deviation of all signal intensity values in CE-T1WI subtraction sequence was 143.45, while that of CE-FLAIR subtraction sequence was 118.41.

CONCLUSION

On CE-FLAIR, brain lymphomas showed relatively poor and homogeneous enhancement, when compared to CE-T1WI. Most brain lymphomas displayed peripheral rim enhancement and central enhancing foci.

Quantitative assessment of normal hip cartilage in children under 9 years old by T2 mapping

OBJECTIVE

To investigate the variation in T2 at different zones of normal hip cartilage in children and the relationship between T2 value and age.

MATERIALS AND METHODS

Nineteen children with 30 normal hip joints were evaluated with a coronal T2 mapping sequence at a 3-Tesla MRI system. The femoral cartilage and acetabular cartilage were firstly segmented by mask-based interactive method and then equally divided into eight and six radial sections, respectively. Moreover, each radial section was further divided into two layers referring to the superficial and deep halves of the corresponding cartilage. Cartilage T2 of these sections and layers were measured and subsequently analyzed.

RESULTS

There was a negative correlation between the T2 values in the hip cartilage and the age of children (r_s < - 0.6, P1 < 0.05). Articular cartilage T2 increased at angles close to the magic angle (54.7°). Femoral cartilage and acetabular cartilage had a relatively shorter T2 in the radial sections near the vertex of the femoral head. The T2 values in superficial layers of both cartilages were significantly higher than those in deep layers (P < 0.05).

CONCLUSION

The T2 value decreases as the cartilage developing into a more mature state. Cartilage T2 values in the weight-bearing areas are relatively low due to an increase of collagen density and the loss of interstitial water. The restriction of the water molecules by solid components in the deeper layer of cartilage may decrease the T2 values.

Non-invasive three-dimensional thickness analysis of oral epithelium based on optical coherence tomography-development and diagnostic performance

Objectives

Evaluating structural changes in oral epithelium can assist with the diagnosis of cancerous lesions. Two-dimensional (2D) non-invasive optical coherence tomography (OCT) is an established technique for this purpose. The objective of this study was to develop and test the diagnostic accuracy of a three-dimensional (3D) evaluation method.

Methods

The oral lip mucosa of 10 healthy volunteers was scanned using an 870-nm spectral-domain OCT device (SD-OCT) with enhanced depth imaging (EDI). Four raters semi-automatically segmented the epithelial layer twice. Thus, eighty 3D datasets were created and analyzed for epithelial thickness. To provide a reference standard for comparison, the raters took cross-sectional 2D measurements at representative sites. The correlation between the 2D and 3D measurements, as well as intra- and inter-rater reliability, were analyzed using intraclass correlation coefficients (ICC).

Results

Mean epithelial thickness was 280 ± 64μm (range 178–500 μm) and 268 ± 49μm (range 163–425 μm) for the 2D and 3D analysis, respectively. The inter-modality correlation of the thickness values was good (ICC: 0.76 [0.626–0.846]), indicating that 3D analysis of epithelial thickness provides valid results. Intra-rater and inter-rater reliability were good (3D analysis) and excellent (2D analysis), suggesting high reproducibility.

Conclusions

Diagnostic accuracy was high for the developed 3D analysis of oral epithelia using non-invasive, radiation-free OCT imaging.

Clinical significance

This new 3D technique could potentially be used to improve time-efficiency and quality in the diagnosis of epithelial lesions compared with the 2D reference standard.

Impact of segmentation and discretization on radiomic features in 68Ga-DOTA-TOC PET/CT images of neuroendocrine tumor

OBJECTIVE

To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from 68Ga-DOTA-TOC PET images in patients with neuroendocrine tumors.

METHODS

Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUVmax fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COVL). The RFs' correlation with volume and SUVmax was analyzed by calculating Pearson's correlation coefficients.

RESULTS

DSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUVmax threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUVmax thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUVmax.

CONCLUSIONS

RFs robustness to manual segmentation resulted higher in NET 68Ga-DOTA-TOC images compared to 18F-FDG PET/CT images. Forty percent SUVmax thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.

Semi-automatic micro-CT segmentation of the midfoot using calibrated thresholds

PURPOSE

In the field of skeletal research, accurate and reliable segmentation methods are necessary for quantitative micro-CT analysis to assess bone quality. We propose a method of semi-automatic image segmentation of the midfoot, using the cuneiform bones as a model, based on thresholds set by phantom calibration that allows reproducible results in low cortical thickness bones.

METHODS

Manual and semi-automatic segmentation methods were compared in micro-CT scans of the medial and intermediate cuneiforms of 24 cadaveric specimens. The manual method used intensity thresholds, hole filling, and manual cleanup. The semi-automatic method utilized calibrated bone and soft tissue thresholds Boolean subtraction to cleanly identify edges before hole filling. Intra- and inter-rater reliability was tested for the semi-automatic method in all specimens. Mask volume and average bone mineral density (BMD) were measured for all masks, and the three-dimensional models were compared to the initial semi-automatic segmentation using an unsigned distance part comparison analysis. Segmentation methods were compared with paired t-tests with significance level 0.05, and reliability was analyzed by calculating intra-class correlation coefficients.

RESULTS

There were statistically significant differences in mask volume and BMD between the manual and semi-automatic segmentation methods in both bones. The intra- and inter-reliability was excellent for mask volume and bone density in both bones. Part comparisons showed a higher maximum distance between surfaces for the manual segmentation than the repeat semi-automatic segmentations.

CONCLUSION

We developed a semi-automatic micro-CT segmentation method based on calibrated thresholds. This method was designed specifically for use in bones with high rates of curvature and low cortical bone density, such as the cuneiforms, where traditional threshold-based segmentation is more challenging. Our method shows improvement over manual segmentation and was highly reliable, making it appropriate for use in quantitative micro-CT analysis.

Advanced CT visualization improves the accuracy of orthopaedic trauma surgeons and residents in classifying proximal humeral fractures: a feasibility study

PURPOSE

Osteosynthesis of proximal humeral fractures remains challenging with high reported failure rates. Understanding the fracture type is mandatory in surgical treatment to achieve an optimal anatomical reduction. Therefore, a better classification ability resulting in improved understanding of the fracture pattern is important for preoperative planning. The purpose was to investigate the feasibility and added value of advanced visualization of segmented 3D computed tomography (CT) images in fracture classification.

METHODS

Seventeen patients treated with either plate-screw-osteosynthesis or shoulder hemi-prosthesis between 2015 and 2019 were included. All preoperative CT scans were segmented to indicate every fracture fragment in a different color. Classification ability was tested in 21 orthopaedic residents and 12 shoulder surgeons. Both groups were asked to classify fractures using three different modalities (standard CT scan, 3D reconstruction model, and 3D segmented model) into three different classification systems (Neer, AO/OTA and LEGO).

RESULTS

All participants were able to classify the fractures more accurately into all three classification systems after evaluating the segmented three-dimensional (3D) models compared to both 2D slice-wise evaluation and 3D reconstruction model. This finding was significant (p < 0.005) with an average success rate of 94%. The participants experienced significantly more difficulties classifying fractures according to the LEGO system than the other two classifications.

CONCLUSION

Segmentation of CT scans added value to the proximal humeral fracture classification, since orthopaedic surgeons were able to classify fractures significantly better into the AO/OTA, Neer, and LEGO classification systems compared to both standard 2D slice-wise evaluation and 3D reconstruction model.

Hippocampal atrophy based Alzheimer's disease diagnosis via machine learning methods

Alzheimer's disease is the most common form of dementia and is a serious health problem. The disease is expected to increase further in the upcoming years with the increase of the elderly population. Developing new treatments and diagnostic methods is getting more important. In this study, we focused on the early diagnosis of dementia in Alzheimer's disease via analysis of neuroimages. We analyzed the data diagnosed by the Alzheimer's Disease Neuroimaging Initiative (ADNI) protocol. The analyzed data were T1-weighted magnetic resonance images of 159 patients with Alzheimer's disease, 217 patients with mild cognitive impairment and 109 cognitively healthy older people. In this study, we propose that the volumetric reduction in the hippocampus is the most important indicator of Alzheimer's disease. There is not much research about the relationship between the volumetric reduction in the hippocampus and Alzheimer's disease. This volume information was calculated through semi-automatic segmentation software ITK-SNAP and a data set was created based on age, gender, diagnosis, and right and left hippocampal volume values. The diagnosis via hippocampal volume information was made by using machine learning techniques. By using this approach, we conclude that brain MRIs can be used to distinguish the patients with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) from each other; while most of the studies were only able to distinguish AD from CN. Our results have revealed that our approach improves the performance of the computer-aided diagnosis of Alzheimer's disease.

Quantification of tumor burden in multiple myeloma by atlas-based semi-automatic segmentation of WB-DWI

Background

Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance by comparing it to the manual segmentations performed by radiologists.

Methods

An atlas-based segmentation was developed to remove the high-signal intensity normal tissues on WB-DWI and to restrict the lesion area to the skeleton. Then, an outlier threshold-based segmentation was applied to WB-DWI images, and the segmented area’s signal intensity was compared to the average signal intensity of a low-fat muscle on T1-weighted images. This method was validated in 22 whole-body DWI images of patients diagnosed with MM. Dice similarity coefficient (DSC), sensitivity and positive predictive value (PPV) were computed to evaluate the SA performance against the gold standard (GS) and to compare with the radiologists. A non-parametric Wilcoxon test was also performed. Apparent diffusion coefficient (ADC) histogram metrics and lesion volume were extracted for the GS segmentation and for the correctly identified lesions by SA and their correlation was assessed.

Results

The mean inter-radiologists DSC was 0.323 ± 0.268. The SA vs GS achieved a DSC of 0.274 ± 0.227, sensitivity of 0.764 ± 0.276 and PPV 0.217 ± 0.207. Its distribution was not significantly different from the mean DSC of inter-radiologist segmentation (p = 0.108, Wilcoxon test). ADC and lesion volume intraclass correlation coefficient (ICC) of the GS and of the correctly identified lesions by the SA was 0.996 for the median and 0.894 for the lesion volume (p < 0.001). The duration of the lesion volume segmentation by the SA was, on average, 10.22 ± 0.86 min, per patient.

Conclusions

The SA provides equally reproducible segmentation results when compared to the manual segmentation of radiologists. Thus, the proposed method offers robust and efficient segmentation of MM lesions on WB-DWI. This method may aid accurate assessment of tumor burden and therefore provide insights to treatment response assessment.

A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans

Many studies have shown that epicardial fat is associated with a higher risk of heart diseases. Accurate epicardial adipose tissue quantification is still an open research issue. Considering that manual approaches are generally user-dependent and time-consuming, computer-assisted tools can considerably improve the result repeatability as well as reduce the time required for performing an accurate segmentation. Unfortunately, fully automatic strategies might not always identify the Region of Interest (ROI) correctly. Moreover, they could require user interaction for handling unexpected events. This paper proposes a semi-automatic method for Epicardial Fat Volume (EFV) segmentation and quantification. Unlike supervised Machine Learning approaches, the method does not require any initial training or modeling phase to set up the system. As a further key novelty, the method also yields a subdivision into quartiles of the adipose tissue density. Quartile-based analysis conveys information about fat densities distribution, enabling an in-depth study towards a possible correlation between fat amounts, fat distribution, and heart diseases. Experimental tests were performed on 50 Calcium Score (CaSc) series and 95 Coronary Computed Tomography Angiography (CorCTA) series. Area-based and distance-based metrics were used to evaluate the segmentation accuracy, by obtaining Dice Similarity Coefficient (DSC) = 93.74% and Mean Absolute Distance (MAD) = 2.18 for CaSc, as well as DSC = 92.48% and MAD = 2.87 for CorCTA. Moreover, the Pearson and Spearman coefficients were computed for quantifying the correlation between the ground-truth EFV and the corresponding automated measurement, by obtaining 0.9591 and 0.9490 for CaSc, and 0.9513 and 0.9319 for CorCTA, respectively. In conclusion, the proposed EFV quantification and analysis method represents a clinically useable tool assisting the cardiologist to gain insights into a specific clinical scenario and leading towards personalized diagnosis and therapy.

User-Guided Segmentation of Multi-modality Medical Imaging Datasets with ITK-SNAP

ITK-SNAP is an interactive software tool for manual and semi-automatic segmentation of 3D medical images. This paper summarizes major new features added to ITK-SNAP over the last decade. The main focus of the paper is on new features that support semi-automatic segmentation of multi-modality imaging datasets, such as MRI scans acquired using different contrast mechanisms (e.g., T1, T2, FLAIR). The new functionality uses decision forest classifiers trained interactively by the user to transform multiple input image volumes into a foreground/background probability map; this map is then input as the data term to the active contour evolution algorithm, which yields regularized surface representations of the segmented objects of interest. The new functionality is evaluated in the context of high-grade and low-grade glioma segmentation by three expert neuroradiogists and a non-expert on a reference dataset from the MICCAI 2013 Multi-Modal Brain Tumor Segmentation Challenge (BRATS). The accuracy of semi-automatic segmentation is competitive with the top specialized brain tumor segmentation methods evaluated in the BRATS challenge, with most results obtained in ITK-SNAP being more accurate, relative to the BRATS reference manual segmentation, than the second-best performer in the BRATS challenge; and all results being more accurate than the fourth-best performer. Segmentation time is reduced over manual segmentation by 2.5 and 5 times, depending on the rater. Additional experiments in interactive placenta segmentation in 3D fetal ultrasound illustrate the generalizability of the new functionality to a different problem domain.

A potential field segmentation based method for tumor segmentation on multi-parametric MRI of glioma cancer patients

Background

Accurate segmentation of brain tumors is vital for the gross tumor volume (GTV) definition in radiotherapy. Functional MR images like apparent diffusion constant (ADC) and fractional anisotropy (FA) images can provide more comprehensive information for sensitive detection of the GTV. We synthesize anatomical and functional MRI for accurate and semi-automatic segmentation of GTVs and improvement of clinical efficiency.

Methods

Four MR image sets including T1-weighted contrast-enhanced (T1C), T2-weighted (T2), apparent diffusion constant (ADC) and fractional anisotropy (FA) images of 5 glioma patients were acquired and registered. A new potential field segmentation (PFS) method was proposed based on the concept of potential field in physics. For T1C, T2 and ADC images, global potential field segmentation (global-PFS) was used on user defined region of interest (ROI) for rough segmentation and then morphologically processed for accurate delineation of the GTV. For FA images, white matter (WM) was removed using local potential field segmentation (local-PFS), and then tumor extent was delineated with region growing and morphological methods. The individual segmentations of multi-parametric images were ensembled into a fused segmentation, considered as final GTV. GTVs were compared with manually delineated ground truth and evaluated with segmentation quality measure (Q), Dice’s similarity coefficient (DSC) and Sensitivity and Specificity.

Results

Experimental study with the five patients’ data and new method showed that, the mean values of Q, DSC, Sensitivity and Specificity were 0.80 (±0.07), 0.88 (±0.04), 0.92 (±0.01) and 0.88 (±0.05) respectively. The global-PFS used on ROIs of T1C, T2 and ADC images can avoid interferences from skull and other non-tumor areas. Similarity to local-PFS on FA images, it can also reduce the time complexity as compared with the global-PFS on whole image sets.

Conclusions

Efficient and semi-automatic segmentation of the GTV can be achieved with the new method. Combination of anatomical and functional MR images has the potential to provide new methods and ideas for target definition in radiotherapy.

Semi-automatic detection of myocardial trabeculation using cardiovascular magnetic resonance: correlation with histology and reproducibility in a mouse model of non-compaction

BACKGROUND

The definition of left ventricular (LV) non-compaction is controversial, and discriminating between normal and excessive LV trabeculation remains challenging. Our goal was to quantify LV trabeculation on cardiovascular magnetic resonance (CMR) images in a genetic mouse model of non-compaction using a dedicated semi-automatic software package and to compare our results to the histology used as a gold standard.

METHODS

Adult mice with ventricular non-compaction were generated by conditional trabecular deletion of Nkx2-5. Thirteen mice (5 controls, 8 Nkx2-5 mutants) were included in the study. Cine CMR series were acquired in the mid LV short axis plane (resolution 0.086 × 0.086x1mm3) (11.75 T). In a sub set of 6 mice, 5 to 7 cine CMR were acquired in LV short axis to cover the whole LV with a lower resolution (0.172 × 0.172x1mm3). We used semi-automatic software to quantify the compacted mass (Mc), the trabeculated mass (Mt) and the percentage of trabeculation (Mt/Mc) on all cine acquisitions. After CMR all hearts were sliced along the short axis and stained with eosin, and histological LV contouring was performed manually, blinded from the CMR results, and Mt, Mc and Mt/Mc were quantified. Intra and interobserver reproducibility was evaluated by computing the intra class correlation coefficient (ICC).

RESULTS

Whole heart acquisition showed no statistical significant difference between trabeculation measured at the basal, midventricular and apical parts of the LV. On the mid-LV cine CMR slice, the median Mt was 0.92 mg (range 0.07-2.56 mg), Mc was 12.24 mg (9.58-17.51 mg), Mt/Mc was 6.74% (0.66-17.33%). There was a strong correlation between CMR and the histology for Mt, Mc and Mt/ Mc with respectively: r2 = 0.94 (p < 0.001), r2 = 0.91 (p < 0.001), r2 = 0.83 (p < 0.001). Intra- and interobserver reproducibility was 0.97 and 0.8 for Mt; 0.98 and 0.97 for Mc; 0.96 and 0.72 for Mt/Mc, respectively and significantly more trabeculation was observed in the Mc Mutant mice than the controls.

CONCLUSION

The proposed semi-automatic quantification software is accurate in comparison to the histology and reproducible in evaluating Mc, Mt and Mt/ Mc on cine CMR.

User Interaction in Semi-Automatic Segmentation of Organs at Risk: a Case Study in Radiotherapy. J Digit Imaging. 2016;29:264-277. [PMID: 26553109 PMCID: PMC4788616 DOI: 10.1007/s10278-015-9839-8]

Accurate segmentation of organs at risk is an important step in radiotherapy planning. Manual segmentation being a tedious procedure and prone to inter- and intra-observer variability, there is a growing interest in automated segmentation methods. However, automatic methods frequently fail to provide satisfactory result, and post-processing corrections are often needed. Semi-automatic segmentation methods are designed to overcome these problems by combining physicians' expertise and computers' potential. This study evaluates two semi-automatic segmentation methods with different types of user interactions, named the "strokes" and the "contour", to provide insights into the role and impact of human-computer interaction. Two physicians participated in the experiment. In total, 42 case studies were carried out on five different types of organs at risk. For each case study, both the human-computer interaction process and quality of the segmentation results were measured subjectively and objectively. Furthermore, different measures of the process and the results were correlated. A total of 36 quantifiable and ten non-quantifiable correlations were identified for each type of interaction. Among those pairs of measures, 20 of the contour method and 22 of the strokes method were strongly or moderately correlated, either directly or inversely. Based on those correlated measures, it is concluded that: (1) in the design of semi-automatic segmentation methods, user interactions need to be less cognitively challenging; (2) based on the observed workflows and preferences of physicians, there is a need for flexibility in the interface design; (3) the correlated measures provide insights that can be used in improving user interaction design.

Optimizing the 3D-reconstruction technique for serial block-face scanning electron microscopy

BACKGROUND

Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern.

NEW METHOD

The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation.

RESULTS

For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result.

COMPARISON WITH EXISTING METHODS

Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity.

CONCLUSION

Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons.

A novel ultrasound based approach for lesion segmentation and its applications in gynecological laparoscopic surgery

Laparoscopic ultrasound (LUS) has been widely utilized as a surgical aide in general, urological, and gynecological applications. Our study summarizes the clinical applications of laparoscopic ultrasonography in laparoscopic gynecologic surgery. Retrospective analyses were performed on 42 women subjects using laparoscopic surgery during laparoscopic extirpation and excision of gynecological tumors in our hospital from August 2011 to August 2013. Specifically, the Esaote 7.5 × 10 MHz laparoscopic transducer was used to detect small residual lesions, as well as to assess, locate and guide in removing the lesions during laparoscopic operations. The findings of LUS were compared with those of preoperative trans-vaginal ultrasound, postoperative, and pathohistological examinations. In addition, a novel method for lesion segmentation was proposed in order to facilitate the laparoscopic gynecologic surgery. In our experiment, laparoscopic operation was performed using a higher frequency and more close to pelvic organs via laparoscopic access. LUS facilitates the ability of gynaecologists to find small residual lesions under laparoscopic visualization and their accurate diagnosis. LUS also helps to locate residual lesions precisely and provides guidance for the removal of residual tumor and eliminate its recurrence effectively. Our experiment provides a safer and more valuable assistance for clinical applications in laparoscopic gynecological surgery that are superior to trans-abdominal ultrasound and trans-vaginal ultrasound.

Efficient semi-automatic 3D segmentation for neuron tracing in electron microscopy images

BACKGROUND

In the area of connectomics, there is a significant gap between the time required for data acquisition and dense reconstruction of the neural processes contained in the same dataset. Automatic methods are able to eliminate this timing gap, but the state-of-the-art accuracy so far is insufficient for use without user corrections. If completed naively, this process of correction can be tedious and time consuming.

NEW METHOD

We present a new semi-automatic method that can be used to perform 3D segmentation of neurites in EM image stacks. It utilizes an automatic method that creates a hierarchical structure for recommended merges of superpixels. The user is then guided through each predicted region to quickly identify errors and establish correct links.

RESULTS

We tested our method on three datasets with both novice and expert users. Accuracy and timing were compared with published automatic, semi-automatic, and manual results.

COMPARISON WITH EXISTING METHODS

Post-automatic correction methods have also been used in Mishchenko et al. (2010) and Haehn et al. (2014). These methods do not provide navigation or suggestions in the manner we present. Other semi-automatic methods require user input prior to the automatic segmentation such as Jeong et al. (2009) and Cardona et al. (2010) and are inherently different than our method.

CONCLUSION

Using this method on the three datasets, novice users achieved accuracy exceeding state-of-the-art automatic results, and expert users achieved accuracy on par with full manual labeling but with a 70% time improvement when compared with other examples in publication.

A modular hierarchical approach to 3D electron microscopy image segmentation

The study of neural circuit reconstruction, i.e., connectomics, is a challenging problem in neuroscience. Automated and semi-automated electron microscopy (EM) image analysis can be tremendously helpful for connectomics research. In this paper, we propose a fully automatic approach for intra-section segmentation and inter-section reconstruction of neurons using EM images. A hierarchical merge tree structure is built to represent multiple region hypotheses and supervised classification techniques are used to evaluate their potentials, based on which we resolve the merge tree with consistency constraints to acquire final intra-section segmentation. Then, we use a supervised learning based linking procedure for the inter-section neuron reconstruction. Also, we develop a semi-automatic method that utilizes the intermediate outputs of our automatic algorithm and achieves intra-segmentation with minimal user intervention. The experimental results show that our automatic method can achieve close-to-human intra-segmentation accuracy and state-of-the-art inter-section reconstruction accuracy. We also show that our semi-automatic method can further improve the intra-segmentation accuracy.

A hybrid semi-automatic method for liver segmentation based on level-set methods using multiple seed points

The present study developed a hybrid semi-automatic method to extract the liver from abdominal computerized tomography (CT) images. The proposed hybrid method consists of a customized fast-marching level-set method for detection of an optimal initial liver region from multiple seed points selected by the user and a threshold-based level-set method for extraction of the actual liver region based on the initial liver region. The performance of the hybrid method was compared with those of the 2D region growing method implemented in OsiriX using abdominal CT datasets of 15 patients. The hybrid method showed a significantly higher accuracy in liver extraction (similarity index, SI=97.6 ± 0.5%; false positive error, FPE = 2.2 ± 0.7%; false negative error, FNE=2.5 ± 0.8%; average symmetric surface distance, ASD=1.4 ± 0.5mm) than the 2D (SI=94.0 ± 1.9%; FPE = 5.3 ± 1.1%; FNE=6.5 ± 3.7%; ASD=6.7 ± 3.8mm) region growing method. The total liver extraction time per CT dataset of the hybrid method (77 ± 10 s) is significantly less than the 2D region growing method (575 ± 136 s). The interaction time per CT dataset between the user and a computer of the hybrid method (28 ± 4 s) is significantly shorter than the 2D region growing method (484 ± 126 s). The proposed hybrid method was found preferred for liver segmentation in preoperative virtual liver surgery planning.