Refinement-cut: user-guided segmentation algorithm for translational science

A Client/Server based Online Environment for the Calculation of Medical Segmentation Scores

Image segmentation plays a major role in medical imaging. Especially in radiology, the detection and development of tumors and other diseases can be supported by image segmentation applications. Tools that provide image segmentation and calculation of segmentation scores are not available at any time for every device due to the size and scope of functionalities they offer. These tools need huge periodic updates and do not properly work on old or weak systems. However, medical use-cases often require fast and accurate results. A complex and slow software can lead to additional stress and thus unnecessary errors. The aim of this contribution is the development of a cross-platform tool for medical segmentation use-cases. The goal is a device-independent and always available possibility for medical imaging including manual segmentation and metric calculation. The result is Studierfenster (studierfenster.at), a web-tool for manual segmentation and segmentation metric calculation. In this contribution, the focus lies on the segmentation metric calculation part of the tool. It provides the functionalities of calculating directed and undirected Hausdorff Distance (HD) and Dice Similarity Coefficient (DSC) scores for two uploaded volumes, filtering for specific values, searching for specific values in the calculated metrics and exporting filtered metric lists in different file formats.

A review on multiplatform evaluations of semi-automatic open-source based image segmentation for cranio-maxillofacial surgery

BACKGROUND AND OBJECTIVES

Computer-assisted technologies, such as image-based segmentation, play an important role in the diagnosis and treatment support in cranio-maxillofacial surgery. However, although many segmentation software packages exist, their clinical in-house use is often challenging due to constrained technical, human or financial resources. Especially technological solutions or systematic evaluations of open-source based segmentation approaches are lacking. The aim of this contribution is to assess and review the segmentation quality and the potential clinical use of multiple commonly available and license-free segmentation methods on different medical platforms.

METHODS

In this contribution, the quality and accuracy of open-source segmentation methods was assessed on different platforms using patient-specific clinical CT-data and reviewed with the literature. The image-based segmentation algorithms GrowCut, Robust Statistics Segmenter, Region Growing 3D, Otsu & Picking, Canny Segmentation and Geodesic Segmenter were investigated in the mandible on the platforms 3D Slicer, MITK and MeVisLab. Comparisons were made between the segmentation algorithms and the ground truth segmentations of the same anatomy performed by two clinical experts (n = 20). Assessment parameters were the Dice Score Coefficient (DSC), the Hausdorff Distance (HD), and Pearsons correlation coefficient (r).

RESULTS

The segmentation accuracy was highest with the GrowCut (DSC 85.6%, HD 33.5 voxel) and the Canny (DSC 82.1%, HD 8.5 voxel) algorithm. Statistical differences between the assessment parameters were not significant (p < 0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the segmentation methods and the ground truth schemes. Functionally stable and time-saving segmentations were observed.

CONCLUSION

High quality image-based semi-automatic segmentation was provided by the GrowCut and the Canny segmentation method. In the cranio-maxillofacial complex, these segmentation methods provide algorithmic alternatives for image-based segmentation in the clinical practice for e.g. surgical planning or visualization of treatment results and offer advantages through their open-source availability. This is the first systematic multi-platform comparison that evaluates multiple license-free, open-source segmentation methods based on clinical data for the improvement of algorithms and a potential clinical use in patient-individualized medicine. The results presented are reproducible by others and can be used for clinical and research purposes.

Clinical evaluation of semi-automatic open-source algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action

INTRODUCTION

Computer assisted technologies based on algorithmic software segmentation are an increasing topic of interest in complex surgical cases. However-due to functional instability, time consuming software processes, personnel resources or licensed-based financial costs many segmentation processes are often outsourced from clinical centers to third parties and the industry. Therefore, the aim of this trial was to assess the practical feasibility of an easy available, functional stable and licensed-free segmentation approach to be used in the clinical practice.

MATERIAL AND METHODS

In this retrospective, randomized, controlled trail the accuracy and accordance of the open-source based segmentation algorithm GrowCut was assessed through the comparison to the manually generated ground truth of the same anatomy using 10 CT lower jaw data-sets from the clinical routine. Assessment parameters were the segmentation time, the volume, the voxel number, the Dice Score and the Hausdorff distance.

RESULTS

Overall semi-automatic GrowCut segmentation times were about one minute. Mean Dice Score values of over 85% and Hausdorff Distances below 33.5 voxel could be achieved between the algorithmic GrowCut-based segmentations and the manual generated ground truth schemes. Statistical differences between the assessment parameters were not significant (p<0.05) and correlation coefficients were close to the value one (r > 0.94) for any of the comparison made between the two groups.

DISCUSSION

Complete functional stable and time saving segmentations with high accuracy and high positive correlation could be performed by the presented interactive open-source based approach. In the cranio-maxillofacial complex the used method could represent an algorithmic alternative for image-based segmentation in the clinical practice for e.g. surgical treatment planning or visualization of postoperative results and offers several advantages. Due to an open-source basis the used method could be further developed by other groups or specialists. Systematic comparisons to other segmentation approaches or with a greater data amount are areas of future works.

Vertebral body segmentation with GrowCut: Initial experience, workflow and practical application

Objectives:

Spinal diseases are very common; for example, the risk of osteoporotic fracture is 40% for White women and 13% for White men in the United States during their lifetime. Hence, the total number of surgical spinal treatments is on the rise with the aging population, and accurate diagnosis is of great importance to avoid complications and a reappearance of the symptoms. Imaging and analysis of a vertebral column is an exhausting task that can lead to wrong interpretations. The overall goal of this contribution is to study a cellular automata-based approach for the segmentation of vertebral bodies between the compacta and surrounding structures yielding to time savings and reducing interpretation errors.

Methods:

To obtain the ground truth, T2-weighted magnetic resonance imaging acquisitions of the spine were segmented in a slice-by-slice procedure by several neurosurgeons. Subsequently, the same vertebral bodies have been segmented by a physician using the cellular automata approach GrowCut.

Results:

Manual and GrowCut segmentations have been evaluated against each other via the Dice Score and the Hausdorff distance resulting in 82.99% ± 5.03% and 18.91 ± 7.2 voxel, respectively. Moreover, the times have been determined during the slice-by-slice and the GrowCut course of actions, indicating a significantly reduced segmentation time (5.77 ± 0.73 min) of the algorithmic approach.

Conclusion:

In this contribution, we used the GrowCut segmentation algorithm publicly available in three-dimensional Slicer for three-dimensional segmentation of vertebral bodies. To the best of our knowledge, this is the first time that the GrowCut method has been studied for the usage of vertebral body segmentation. In brief, we found that the GrowCut segmentation times were consistently less than the manual segmentation times. Hence, GrowCut provides an alternative to a manual slice-by-slice segmentation process.

Computer-aided planning and reconstruction of cranial 3D implants

In this contribution, a prototype for semiautomatic computer-aided planning and reconstruction of cranial 3D Implants is presented. The software prototype guides the user through the workflow, beginning with loading and mirroring the patient's head to obtain an initial curvature of the cranial implant. However, naïve mirroring is not sufficient for an implant, because human heads are in general too asymmetric. Thus, the user can perform Laplacian smoothing, followed by Delaunay triangulation, for generating an aesthetic looking and well-fitting implant. Finally, our software prototype allows to save the designed 3D model of the implant as a STL-file for 3D printing. The 3D printed implant can be used for further pre-interventional planning or even as the final implant for the patient. In summary, our findings show that a customized MeVisLab prototype can be an alternative to complex commercial planning software, which may not be available in a clinic.

Interactive Outlining of Pancreatic Cancer Liver Metastases in Ultrasound Images

Ultrasound (US) is the most commonly used liver imaging modality worldwide. Due to its low cost, it is increasingly used in the follow-up of cancer patients with metastases localized in the liver. In this contribution, we present the results of an interactive segmentation approach for liver metastases in US acquisitions. A (semi-) automatic segmentation is still very challenging because of the low image quality and the low contrast between the metastasis and the surrounding liver tissue. Thus, the state of the art in clinical practice is still manual measurement and outlining of the metastases in the US images. We tackle the problem by providing an interactive segmentation approach providing real-time feedback of the segmentation results. The approach has been evaluated with typical US acquisitions from the clinical routine, and the datasets consisted of pancreatic cancer metastases. Even for difficult cases, satisfying segmentations results could be achieved because of the interactive real-time behavior of the approach. In total, 40 clinical images have been evaluated with our method by comparing the results against manual ground truth segmentations. This evaluation yielded to an average Dice Score of 85% and an average Hausdorff Distance of 13 pixels.

FISICO: Fast Image SegmentatIon COrrection

Background and Purpose

In clinical diagnosis, medical image segmentation plays a key role in the analysis of pathological regions. Despite advances in automatic and semi-automatic segmentation techniques, time-effective correction tools are commonly needed to improve segmentation results. Therefore, these tools must provide faster corrections with a lower number of interactions, and a user-independent solution to reduce the time frame between image acquisition and diagnosis.

Methods

We present a new interactive method for correcting image segmentations. Our method provides 3D shape corrections through 2D interactions. This approach enables an intuitive and natural corrections of 3D segmentation results. The developed method has been implemented into a software tool and has been evaluated for the task of lumbar muscle and knee joint segmentations from MR images.

Results

Experimental results show that full segmentation corrections could be performed within an average correction time of 5.5±3.3 minutes and an average of 56.5±33.1 user interactions, while maintaining the quality of the final segmentation result within an average Dice coefficient of 0.92±0.02 for both anatomies. In addition, for users with different levels of expertise, our method yields a correction time and number of interaction decrease from 38±19.2 minutes to 6.4±4.3 minutes, and 339±157.1 to 67.7±39.6 interactions, respectively.

RFA-cut: Semi-automatic segmentation of radiofrequency ablation zones with and without needles via optimal s-t-cuts

In this contribution, we present a semi-automatic segmentation algorithm for radiofrequency ablation (RFA) zones via optimal s-t-cuts. Our interactive graph-based approach builds upon a polyhedron to construct the graph and was specifically designed for computed tomography (CT) acquisitions from patients that had RFA treatments of Hepatocellular Carcinomas (HCC). For evaluation, we used twelve post-interventional CT datasets from the clinical routine and as evaluation metric we utilized the Dice Similarity Coefficient (DSC), which is commonly accepted for judging computer aided medical segmentation tasks. Compared with pure manual slice-by-slice expert segmentations from interventional radiologists, we were able to achieve a DSC of about eighty percent, which is sufficient for our clinical needs. Moreover, our approach was able to handle images containing (DSC=75.9%) and not containing (78.1%) the RFA needles still in place. Additionally, we found no statistically significant difference (p<;0.423) between the segmentation results of the subgroups for a Mann-Whitney test. Finally, to the best of our knowledge, this is the first time a segmentation approach for CT scans including the RFA needles is reported and we show why another state-of-the-art segmentation method fails for these cases. Intraoperative scans including an RFA probe are very critical in the clinical practice and need a very careful segmentation and inspection to avoid under-treatment, which may result in tumor recurrence (up to 40%). If the decision can be made during the intervention, an additional ablation can be performed without removing the entire needle. This decreases the patient stress and associated risks and costs of a separate intervention at a later date. Ultimately, the segmented ablation zone containing the RFA needle can be used for a precise ablation simulation as the real needle position is known.