A convenient and stable vertebrae instance segmentation method for transforaminal endoscopic surgery planning

Development of a software system for surgical robots based on multimodal image fusion: study protocol

Background

Surgical robots are gaining increasing popularity because of their capability to improve the precision of pedicle screw placement. However, current surgical robots rely on unimodal computed tomography (CT) images as baseline images, limiting their visualization to vertebral bone structures and excluding soft tissue structures such as intervertebral discs and nerves. This inherent limitation significantly restricts the applicability of surgical robots. To address this issue and further enhance the safety and accuracy of robot-assisted pedicle screw placement, this study will develop a software system for surgical robots based on multimodal image fusion. Such a system can extend the application range of surgical robots, such as surgical channel establishment, nerve decompression, and other related operations.

Methods

Initially, imaging data of the patients included in the study are collected. Professional workstations are employed to establish, train, validate, and optimize algorithms for vertebral bone segmentation in CT and magnetic resonance (MR) images, intervertebral disc segmentation in MR images, nerve segmentation in MR images, and registration fusion of CT and MR images. Subsequently, a spine application model containing independent modules for vertebrae, intervertebral discs, and nerves is constructed, and a software system for surgical robots based on multimodal image fusion is designed. Finally, the software system is clinically validated.

Discussion

We will develop a software system based on multimodal image fusion for surgical robots, which can be applied to surgical access establishment, nerve decompression, and other operations not only for robot-assisted nail placement. The development of this software system is important. First, it can improve the accuracy of pedicle screw placement, percutaneous vertebroplasty, percutaneous kyphoplasty, and other surgeries. Second, it can reduce the number of fluoroscopies, shorten the operation time, and reduce surgical complications. In addition, it would be helpful to expand the application range of surgical robots by providing key imaging data for surgical robots to realize surgical channel establishment, nerve decompression, and other operations.

Lumbar spine segmentation in MR images: a dataset and a public benchmark

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. Performance values were computed on a sequestered set of 39 studies with 97 series, which were additionally used to set up a continuous segmentation challenge that allows for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation and improve the diagnostic value of lumbar spine MRI.

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.

Morphometric change in intervertebral foramen after percutaneous endoscopic lumbar foraminotomy: an in vivo radiographic study based on three-dimensional foramen reconstruction

PURPOSE

This study aimed to perform in vivo three-dimensional (3D) quantitative measurements of morphometric changes in the foramen in patients with lumbar foraminal stenosis (LFS) undergoing percutaneous endoscopic lumbar foraminotomy (PELF) and investigate the relationship between anatomical changes in the foramen and clinical outcomes.

METHODS

We retrospectively reviewed consecutive patients with LFS treated with PELF between January 2016 and September 2020 at our centre. Clinical outcomes were evaluated. Foraminal volume (FV) and foraminal minimal area (FMA) were calculated using a novel vertebral and foramen segmentation method. A comparison of the anatomical parameters of the foramen were conducted between the satisfied and unsatisfied groups divided based on the modified MacNab criteria.

RESULTS

A total of 26 eligible patients with a mean follow-up of 3.6 years were enrolled. A significant increase was found in overall FV (71.5%) from 1.436 ± 0.396 to 2.464 ± 0.719 cm3 (P < 0.001) and FMA (109.5%) from 0.849 ± 0.207 to 1.780 ± 0.524 cm2. All clinical outcomes were significantly improved (P < 0.001) after PELF. No significant difference was found in changes in neither FV nor FMA between the two groups.

CONCLUSION

Clinical results and foraminal dimensions improved significantly after PELF, indicating that PELF was a prominent technique suitable for LFS because of the direct decompression at impingement structures. No relationship was found between morphometric changes and clinical outcomes, revealing that full-scale endoscopic decompression is necessary and adequate for LFS, and unsatisfactory outcomes are less likely to result from decompression procedure.