Nava Aghdasi

Evaluation of segmentation methods on head and neck CT: Auto‐segmentation challenge 2015

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.

Efficient orbital structures segmentation with prior anatomical knowledge

Abstract. We present a fully automatic method for segmenting orbital structures (globes, optic nerves, and extraocular muscles) in CT images. Prior anatomical knowledge, such as shape, intensity, and spatial relationships of organs and landmarks, were utilized to define a volume of interest (VOI) that contains the desired structures. Then, VOI was used for fast localization and successful segmentation of each structure using predefined rules. Testing our method with 30 publicly available datasets, the average Dice similarity coefficient for right and left sides of [0.81, 0.79] eye globes, [0.72, 0.79] optic nerves, and [0.73, 0.76] extraocular muscles were achieved. The proposed method is accurate, efficient, does not require training data, and its intuitive pipeline allows the user to modify or extend to other structures.

Automated Surgical Approach Planning for Complex Skull Base Targets: Development and Validation of a Cost Function and Semantic At-las:

Successful multidisciplinary treatment of skull base pathology requires precise preoperative planning. Current surgical approach (pathway) selection for these complex procedures depends on an individual surgeon’s experiences and background training. Because of anatomical variation in both normal tissue and pathology (eg, tumor), a successful surgical pathway used on one patient is not necessarily the best approach on another patient. The question is how to define and obtain optimized patient-specific surgical approach pathways? In this article, we demonstrate that the surgeon’s knowledge and decision making in preoperative planning can be modeled by a multiobjective cost function in a retrospective analysis of actual complex skull base cases. Two different approaches— weighted-sum approach and Pareto optimality—were used with a defined cost function to derive optimized surgical pathways based on preoperative computed tomography (CT) scans and manually designated pathology. With the first method, surgeon’s preferences were input as a set of weights for each objective before the search. In the second approach, the surgeon’s preferences were used to select a surgical pathway from the computed Pareto optimal set. Using preoperative CT and magnetic resonance imaging, the patient-specific surgical pathways derived by these methods were similar (85% agreement) to the actual approaches performed on patients. In one case where the actual surgical approach was different, revision surgery was required and was performed utilizing the computationally derived approach pathway.

Quantitative Analysis of Transnasal Anterior Skull Base Approach: Report of Technology for Intraoperative Assessment of Instrument Motion:

Objective: To develop a method to measure intraoperative surgical instrument motion. This model will be applicable to the study of surgical instrument kinematics including surgical training, skill verification, and the development of surgical warning systems that detect aberrant instrument motion that may result in patient injury. Design: We developed an algorithm to automate derivation of surgical instrument kinematics in an endoscopic endonasal skull base surgery model. Surgical instrument motion was recorded during a cadaveric endoscopic transnasal approach to the pituitary using a navigation system modified to record intraoperative time-stamped Euclidian coordinates and Euler angles. Microdebrider tip coordinates and angles were referenced to the cadaver’s preoperative computed tomography scan allowing us to assess surgical instrument kinematics over time. A representative cadaveric endoscopic endonasal approach to the pituitary was performed to demonstrate feasibility of our algorithm for deriving surgical instrument kinematics. Conclusions: Technical feasibility of automatically measuring intraoperative surgical instrument motion and deriving kinematics measurements was demonstrated using standard navigation equipment.

Wrist Motion Variation between Novices and Experienced Surgeons Performing Simulated Airway Surgery

Objective To determine whether wrist motion measured by a smartphone application can be used as a performance metric for a simulated airway procedure requiring both wrist and finger dexterity. We hypothesized that this accelerometer application could detect differences between novices and experienced surgeons performing simulated cricothyrotomy. Setting Academic medical center. Study Design Prospective pilot cohort study. Methods Voluntary surgeons and nonsurgeons were recruited. After viewing a training video, smartphones with accelerometer applications were attached to both wrists while subjects performed a cricothyrotomy on a validated task trainer. Procedure time and motion parameters, including average resultant acceleration (ARA), total resultant acceleration (TRA), and suprathreshold acceleration events (STAEs), were collected for dominant and nondominant hands. Subjects were stratified by prior experience. Blinded experts scored each performance using Objective Structured Assessment of Technical Skills (OSATS), and t tests were used to compare performance. Results Thirty subjects were enrolled. Median age was 26 years, and 20 subjects were male. In the dominant hand, significant differences were seen between novice and experienced surgeons in TRA (P = .005) and procedure time (P = .006), while no significant differences were seen in STAEs (P = .42) and ARA (P = .33). In the nondominant hand, all variables were significantly different between the 2 groups: STAEs (P = .012), ARA (P = .007), TRA (P = .004), and procedure time (P = .006). Conclusions Wrist motion measured by a low-cost smartphone application can distinguish between novice and experienced surgeons performing simulated airway surgery. This tool provides cost-effective and objective performance feedback.

Atlas and feature based 3D pathway visualization enhancement for skull base pre-operative fast planning from head CT

Minimally invasive neuroendoscopic surgery provides an alternative to open craniotomy for many skull base lesions. These techniques provides a great benefit to the patient through shorter ICU stays, decreased post-operative pain and quicker return to baseline function. However, density of critical neurovascular structures at the skull base makes planning for these procedures highly complex. Furthermore, additional surgical portals are often used to improve visualization and instrument access, which adds to the complexity of pre-operative planning. Surgical approach planning is currently limited and typically involves review of 2D axial, coronal, and sagittal CT and MRI images. In addition, skull base surgeons manually change the visualization effect to review all possible approaches to the target lesion and achieve an optimal surgical plan. This cumbersome process relies heavily on surgeon experience and it does not allow for 3D visualization. In this paper, we describe a rapid pre-operative planning system for skull base surgery using the following two novel concepts: importance-based highlight and mobile portal. With this innovation, critical areas in the 3D CT model are highlighted based on segmentation results. Mobile portals allow surgeons to review multiple potential entry portals in real-time with improved visualization of critical structures located inside the pathway. To achieve this we used the following methods: (1) novel bone-only atlases were manually generated, (2) orbits and the center of the skull serve as features to quickly pre-align the patient’s scan with the atlas, (3) deformable registration technique was used for fine alignment, (4) surgical importance was assigned to each voxel according to a surgical dictionary, and (5) pre-defined transfer function was applied to the processed data to highlight important structures. The proposed idea was fully implemented as independent planning software and additional data are used for verification and validation. The experimental results show: (1) the proposed methods provided greatly improved planning efficiency while optimal surgical plans were successfully achieved, (2) the proposed methods successfully highlighted important structures and facilitated planning, (3) the proposed methods require shorter processing time than classical segmentation algorithms, and (4) these methods can be used to improve surgical safety for surgical robots.