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Purpose/objective(s) Tumor Treating Fields (TTFields) is an FDA-approved treatment for glioblastoma multiforme (GBM) and malignant pleural mesothelioma (MPM). Moreover, TTFields therapy is currently investigated in a phase III clinical trial for the treatment of advanced Non-Small Cell Lung Cancer (NSCLC). Recent studies have shown that larger TTFields dose was associated with longer patients' survival. Therefore, personalized simulations to estimate the dose are performed as part of the patient treatment planning. For MPM and NSCLC treatment, these simulations require the segmentation of all upper torso tissues. A manual segmentation of the torso requires a few dozens of hours per patient and is impractical. Therefore, we have developed a computational method for semi-automatic segmentation of all upper torso tissues that are relevant to TTFields treatment planning. Materials/methods We have incorporated a dataset of 40 CT images of NSCLC patients that underwent TTFields treatment in the lungs for this study. We have utilized threshold-based methods combined with morphological operations, region growing methods and known anatomical spatial relations to automatically identify and segment the lungs, bones, skin, muscle, fat, spinal cavity, costal cartilage, trachea and bronchi. Other structures such as the heart, blood vessels, liver, stomach, spleen, esophagus, diaphragm and intervertebral discs were semi-automatically segmented by using a few reference points that were provided by a human rater. Since there is no gold standard segmentation of the whole torso, an experienced radiation oncologist that is highly familiar with TTFields treatment inspected the results of the algorithm on top of the original CT images. Results The radiation oncologist has confirmed that the semi-automatic segmentation of the torso provides an adequate quality result for TTFields treatment planning for all cases. The segmentation time was reduced to one hour on a typical patient, compared to 20 hours that is the estimated time required for a fully manual segmentation. Conclusion We have presented a method for adequate quality segmentation of the upper torso in a reasonable time to facilitate TTFields treatment planning. In addition, this method facilitates the creation of a dataset for the development of state-of-the-art segmentation methods that utilize deep learning methods.

Purpose/objective(s) Tumor Treating Fields (TTFields) is an FDA-approved treatment for glioblastoma multiforme (GBM) and malignant pleural mesothelioma (MPM), and is currently being investigated in a phase III trial for the treatment of brain metastases from non-small cell lung cancer (NSCLC). Increased TTFields dose density at the tumor is associated with longer patient survival. Therefore, estimating dose at the tumor utilizing computational simulations with patient-specific computational models are integral for treatment planning. NSCLC brain metastases are often observed in infratentorial brain regions. In these cases, TTFields arrays may be placed on the head, neck, and upper back. Manual segmentation of this large area is impractical and time consuming. Therefore, we developed a computational method for semi-automatic segmentation of infratentorial tissues relevant to TTFields treatment planning. Materials/methods We incorporated a dataset of 20 head T1w Gad MRIs of patients treated with TTFields with segmentation of the infratentorial area, and 20 CT images that incorporate head, neck and upper torso from a public database for segmentation of extracranial structures. For the segmentation of the infratentorial zone we developed an atlas-based method that deforms a predefined statistical atlas to best fit the patient's MRI. Our method then revises the initial fitting to ensure the segmented structures adhere to known anatomical relations. For supratentorial brain areas, we incorporate a custom atlas-based method. Extra-cranial zones (head, neck and upper torso) were segmented with threshold-based methods for the skin, muscle, fat, bones, and bronchi. User-defined landmarks were used to segment the esophagus and the artery. The spine and CSF were segmented automatically by identifying the relevant area and defining a constant ratio between their diameters. To evaluate the segmentation of the infratentorial regions, a trained annotator manually segmented the infratentorial area of the 20 head MRIs. We compared manual and automatic segmentations and measured the Dice overlap score. Results The average Dice coefficient between the human annotator and the algorithms' segmentation was 0.84 (SD = 0.05) for cerebellum and 0.67 (SD = 0.05) for brainstem. These results are comparable to those observed in supratentorial regions with a method that was verified previously. The radiation oncologist confirmed that the semi-automatic segmentation of the infratentorial, and extracranial head, neck and upper torso provides a quality result for TTFields treatment planning in a reasonable amount of time for all cases. The infratentorial segmentation is fully automatic and typically lasts a few minutes. Segmentation of extracranial structures is semi-automatic and typically lasts less than an hour. Conclusion We have developed a fast method for the segmentation of infratentorial structures for TTFields planning.

Deep brain stimulation (DBS) has evolved into a powerful clinical therapy for a range of neurological disorders, but even with impressive clinical growth, DBS technology has been relatively stagnant over its history. However, enhanced collaborations between neural engineers, neuroscientists, physicists, neurologists, and neurosurgeons are beginning to address some of the limitations of current DBS technology. These interactions have helped to develop novel ideas for the next generation of clinical DBS systems. This review attempts collate some of that progress and with two goals in mind. First, provide a general description of current clinical DBS practices, geared toward educating biomedical engineers and computer scientists on a field that needs their expertise and attention. Second, describe some of the technological developments that are currently underway in surgical targeting, stimulation parameter selection, stimulation protocols, and stimulation hardware that are being directly evaluated for near term clinical application.

Point-based rigid registration is the method of choice for aligning medical datasets in diagnostic and image-guided surgery systems. The most clinically relevant localization error measure is the Target Registration Error (TRE), which is the distance between the image-defined target and the corresponding target defined on another image or on the physical anatomy after registration. The TRE directly depends on the Fiducial Localization Error (FLE), which is the discrepancy between the selected and the actual (unknown) fiducial locations. Since the actual locations of targets usually cannot be measured after registration, the TRE is often estimated by the Fiducial Registration Error (FRE), which is the RMS distance between the fiducials in both datasets after registration, or with Fitzpatrick's TRE (FTRE) formula. However, low FRE-TRE and FTRE-TRE correlations have been reported in clinical practice and in theoretical studies. In this article, we show that for realistic FLE classes, the TRE and the FRE are uncorrelated, regardless of the target location and the number of fiducials and their configuration, and regardless of the FLE magnitude distribution. We use a geometrical approach and classical invariant theory to model the FLE and derive its relation to the TRE and FRE values. We show that, for these FLE classes, the FTRE and TRE are also uncorrelated. Finally, we show with simulations on clinical data that the FRE-TRE correlation is low also in the neighborhood of the FLE-FRE invariant classes. Consequently, and contrary to common practice, the FRE and FTRE may not always be used as surrogates for the TRE.

This paper describes a novel system for precise automatic targeting in minimally invasive neurosurgery. The system consists of a miniature robot fitted with a rigid mechanical guide for needle, catheter, or probe insertion. Intraoperatively, the robot is directly affixed to the patient skull or to a head clamp. It automatically positions itself with respect to predefined targets in a preoperative CT/MRI image following a three-way anatomical registration with an intraoperative 3D-laser scan of the patient face features. We describe the system architecture, surgical protocol, software modules, and implementation. Registration results on 19 pairs of real MRI and 3D laser scan data show an RMS error of 1.0 mm (std = 0.95 mm) in 2 s.