Qualitative Comparison of Image Stitching Algorithms for Multi-Camera Systems in Laparoscopy

Identifying Responsible Factors for Poor Surgical Visibility in Pediatric Laparoscopic Fundoplication: A Retrospective Single-Center Study

Background: Laparoscopic fundoplication is commonly performed in patients with neurological impairment. However, these patients often have spinal deformities that can complicate achieving a clear surgical view. This study aimed to identify factors associated with poor visibility in pediatric laparoscopic fundoplication. Methods: Operative videos, medical records, and radiographs of patients who underwent laparoscopic fundoplication between 2015 and 2023 were retrospectively reviewed. The videos were reviewed by two pediatric surgeons and classified into good or poor visibility groups. Age, sex, height, weight, history of abdominal surgery, lordosis, operative time, blood loss, and intraoperative complications were compared between the two groups. Lordosis was evaluated using the sagittal view of computed tomography images, and the anterior vertebral depth and abdominal thickness were measured to calculate the ratio. Results: Forty-one patients were included in this study. Based on the video review, the patients were classified into good (20 patients) and poor (21 patients) visibility groups. The median age, height, and weight were 6 years, 110 cm, and 16.1 kg, respectively. In the poor visibility group, 23.8% of patients had a history of abdominal surgery (P = .048). Additionally, the anterior vertebral depth to abdominal thickness ratios at the first and third lumbar vertebrae were significantly lower in the poor visibility group (P = .016 and P = .0018, respectively). There were no significant differences in the operative time, blood loss, or intraoperative complications between the two groups. Conclusions: Lordosis and a history of abdominal surgery may be risk factors for poor visibility in pediatric laparoscopic fundoplication.

Rapid digital pathology of H&E-stained fresh human brain specimens as an alternative to frozen biopsy

BACKGROUND

Hematoxylin and Eosin (H&E)-based frozen section (FS) pathology is presently the global standard for intraoperative tumor assessment (ITA). Preparation of frozen section is labor intensive, which might consume up-to 30 minutes, and is susceptible to freezing artifacts. An FS-alternative technique is thus necessary, which is sectioning-free, artifact-free, fast, accurate, and reliably deployable without machine learning and/or additional interpretation training.

METHODS

We develop a training-free true-H&E Rapid Fresh digital-Pathology (the-RFP) technique which is 4 times faster than the conventional preparation of frozen sections. The-RFP is assisted by a mesoscale Nonlinear Optical Gigascope (mNLOG) platform with a streamlined rapid artifact-compensated 2D large-field mosaic-stitching (rac2D-LMS) approach. A sub-6-minute True-H&E Rapid whole-mount-Soft-Tissue Staining (the-RSTS) protocol is introduced for soft/frangible fresh brain specimens. The mNLOG platform utilizes third harmonic generation (THG) and two-photon excitation fluorescence (TPEF) signals from H and E dyes, respectively, to yield the-RFP images.

RESULTS

We demonstrate the-RFP technique on fresh excised human brain specimens. The-RFP enables optically-sectioned high-resolution 2D scanning and digital display of a 1 cm² area in <120 seconds with 3.6 Gigapixels at a sustained effective throughput of >700 M bits/sec, with zero post-acquisition data/image processing. Training-free blind tests considering 50 normal and tumor-specific brain specimens obtained from 8 participants reveal 100% match to the respective formalin-fixed paraffin-embedded (FFPE)-biopsy outcomes.

CONCLUSIONS

We provide a digital ITA solution: the-RFP, which is potentially a fast and reliable alternative to FS-pathology. With H&E-compatibility, the-RFP eliminates color- and morphology-specific additional interpretation training for a pathologist, and the-RFP-assessed specimen can reliably undergo FFPE-biopsy confirmation.

Optimized Seam-Driven Image Stitching Method Based on Scene Depth Information

It is quite challenging to stitch images with continuous depth changes and complex textures. To solve this problem, we propose an optimized seam-driven image stitching method considering depth, color, and texture information of the scene. Specifically, we design a new energy function to reduce the structural distortion near the seam and improve the invisibility of the seam. By additionally introducing depth information into the smoothing term of energy function, the seam is guided to pass through the continuous regions of the image with high similarity. The experimental results show that benefiting from the new defined energy function, the proposed method can find the seam that adapts to the depth of the scene, and effectively avoid the seam from passing through the salient objects, so that high-quality stitching results can be achieved. The comparison with the representative image stitching methods proves the effectiveness and generalization of the proposed method.