HeiPorSPECTRAL - the Heidelberg Porcine HyperSPECTRAL Imaging Dataset of 20 Physiological Organs.

Hyperspectral Imaging (HSI) is a relatively new medical imaging modality that exploits an area of diagnostic potential formerly untouched. Although exploratory translational and clinical studies exist, no surgical HSI datasets are openly accessible to the general scientific community. To address this bottleneck, this publication releases HeiPorSPECTRAL ( https://www.heiporspectral.org ; https://doi.org/10.5281/zenodo.7737674 ), the first annotated high-quality standardized surgical HSI dataset. It comprises 5,758 spectral images acquired with the TIVITA® Tissue and annotated with 20 physiological porcine organs from 8 pigs per organ distributed over a total number of 11 pigs. Each HSI image features a resolution of 480 × 640 pixels acquired over the 500-1000 nm wavelength range. The acquisition protocol has been designed such that the variability of organ spectra as a function of several parameters including the camera angle and the individual can be assessed. A comprehensive technical validation confirmed both the quality of the raw data and the annotations. We envision potential reuse within this dataset, but also its reuse as baseline data for future research questions outside this dataset. Measurement(s) Spectral Reflectance Technology Type(s) Hyperspectral Imaging Sample Characteristic - Organism Sus scrofa.

Relative Pain Reduction and Duration of Nerve Block Response Predict Outcomes in Headache Surgery: A Prospective Cohort Study.

BACKGROUND: Experts agree that nerve block (NB) response is an important tool in headache surgery screening. However, the predictive value of NBs remains to be proven in a prospective fashion. METHODS: Pre-NB and post-NB visual analogue pain scores (0 to 10) and duration of NB response were recorded prospectively. Surgical outcomes were recorded prospectively by calculating the Migraine Headache Index (MHI) preoperatively and postoperatively at 3 months, 12 months, and every year thereafter. RESULTS: The study population included 115 patients. The chance of achieving MHI percentage improvement of 80% or higher was significantly higher in subjects who reported relative pain reduction of greater than 60% following NB versus less than or equal to 60% [63 of 92 (68.5%) versus 10 of 23 (43.5%); P = 0.03]. Patients were more likely to improve their MHI 50% or more with relative pain reduction of greater than 40% versus 40% or less [82 of 104 (78.8%) versus five of 11 (45.5%); P = 0.01]. In subjects with NB response of greater than 15 days, 10 of 13 patients (77.0%) experienced MHI improvement of 80% or greater. Notably, all of these patients (100%) reported MHI improvement of 50% or greater, with mean MHI improvement of 88%. Subjects with a NB response of 24 hours or more achieved significantly better outcomes than patients with a shorter response (72.7% ± 37.0% versus 46.1% ± 39.7%; P = 0.02). However, of 14 patients reporting NB response of less than 24 hours, four patients had MHI improvement of 80% or greater, and seven, of 50% or greater. CONCLUSIONS: Relative pain reduction and duration of NB response are predictors of MHI improvement after headache surgery. NBs are a valuable tool to identify patients who will benefit from surgery. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, III.

How to attract our future? - Perception of plastic surgery among medical students.

BACKGROUND: There is a mounting body of evidence that underscores the worldwide and US national need for increased plastic surgery recruitment of trainees. Thus, plastic surgery must attract more applicants while maintaining the high-level qualifications of residency candidates. METHODS: A total of 250 (w = 197) medical students rated the prototypical plastic surgeon (PS), general practitioner (GP), and craniomaxillofacial surgeon (CMF) with respect to traits derived from a literature review on the general perception of surgery, favorability, and their intention to pursue a respective career. RESULTS: Factor analysis yielded two overarching dimensions of prototype perception in addition to femininity and resilience, one reflecting a coldhearted, narcissistic, competitive character (status primacy; SP), and one reflecting role-model-like traits (hard-working, healthy, admired, and empathetic). Prototypical PSs scored significantly higher on SP than GPs (t(249) = 18.72, p < 0.001, d = 1.26) and CMFs (t(249) = 5.73, p < 0.001, d = 0.36), while receiving significantly less positive evaluations (GP: t(249) = -9.93, p < 0.001, d = -0.63; CMF: t(249) = -3.52, p < 0.001, d = -0.22). The higher participants rated PSs on SP, the more likely a career in plastic surgery was excluded (OR = 0.71, p = 0.03). An opposite relationship with femininity approached significance (OR = 1.32, p = 0.06). CONCLUSIONS: Given the growing need for PSs, worldwide and US national task fields have to overcome the outdated traits and highlight the field's pro-bono engagement. Furthermore, plastic surgery should further expand its leading role in promoting female trainees.

Artificial intelligence-enabled simulation of gluteal augmentation: A helpful tool in preoperative outcome simulation?

BACKGROUND: While the buttock region is considered an esthetic hallmark, the Brazilian butt lift (BBL) remains controversially discussed in the plastic surgery community. This is due to its contentious safety profile. Thus, informed consent and patient education play a key role in preoperative planning. To this end, we aimed to program an easy-to-use, widely accessible, and low-budget algorithm that produces reliable outcome simulations. METHODS: The conditional generative adversarial network (GAN) was trained using pre- and postoperative images from 1628 BBL patients. To validate outcome simulation, 25 GAN-generated images were assessed deploying 67 Amazon Mechanical Turk Workers (Mturks). RESULTS: Mturks could not differentiate between GAN-generated and real patient images in approximately 49.4% of all trials. CONCLUSION: This study presents a free-to-use, widely accessible, and reliable algorithm to visualize potential surgical outcomes that could potentially be applied in other fields of plastic surgery.

ICG-augmented hyperspectral imaging for visualization of intestinal perfusion compared to conventional ICG fluorescence imaging: an experimental study.

BACKGROUND: Small bowel malperfusion (SBM) can cause high morbidity and severe surgical consequences. However, there is no standardized objective measuring tool for the quantification of SBM. Indocyanine green (ICG) imaging can be used for visualization, but lacks standardization and objectivity. Hyperspectral imaging (HSI) as a newly emerging technology in medicine might present advantages over conventional ICG fluorescence or in combination with it. METHODS: HSI baseline data from physiological small bowel, avascular small bowel and small bowel after intravenous application of ICG was recorded in a total number of 54 in-vivo pig models. Visualizations of avascular small bowel after mesotomy were compared between HSI only (1), ICG-augmented HSI (IA-HSI) (2), clinical evaluation through the eyes of the surgeon (3) and conventional ICG imaging (4). The primary research focus was the localization of resection borders as suggested by each of the four methods. Distances between these borders were measured and histological samples were obtained from the regions in between in order to quantify necrotic changes 6 h after mesotomy for every region. RESULTS: StO2 images (1) were capable of visualizing areas of physiological perfusion and areas of clearly impaired perfusion. However, exact borders where physiological perfusion started to decrease could not be clearly identified. Instead, IA-HSI (2) suggested a sharp-resection line where StO2 values started to decrease. Clinical evaluation (3) suggested a resection line 23 mm (±7 mm) and conventional ICG imaging (4) even suggested a resection line 53 mm (±13 mm) closer towards the malperfused region. Histopathological evaluation of the region that was sufficiently perfused only according to conventional ICG (R3) already revealed a significant increase in pre-necrotic changes in 27% (±9%) of surface area. Therefore, conventional ICG seems less sensitive than IA-HSI with regards to detection of insufficient tissue perfusion. CONCLUSIONS: In this experimental animal study, IA-HSI (2) was superior for the visualization of segmental SBM compared to conventional HSI imaging (1), clinical evaluation (3) or conventional ICG imaging (4) regarding histopathological safety. ICG application caused visual artifacts in the StO2 values of the HSI camera as values significantly increase. This is caused by optical properties of systemic ICG and does not resemble a true increase in oxygenation levels. However, this empirical finding can be used to visualize segmental SBM utilizing ICG as contrast agent in an approach for IA-HSI. Clinical applicability and relevance will have to be explored in clinical trials. LEVEL OF EVIDENCE: Not applicable. Translational animal science. Original article.

Robust deep learning-based semantic organ segmentation in hyperspectral images.

Semantic image segmentation is an important prerequisite for context-awareness and autonomous robotics in surgery. The state of the art has focused on conventional RGB video data acquired during minimally invasive surgery, but full-scene semantic segmentation based on spectral imaging data and obtained during open surgery has received almost no attention to date. To address this gap in the literature, we are investigating the following research questions based on hyperspectral imaging (HSI) data of pigs acquired in an open surgery setting: (1) What is an adequate representation of HSI data for neural network-based fully automated organ segmentation, especially with respect to the spatial granularity of the data (pixels vs. superpixels vs. patches vs. full images)? (2) Is there a benefit of using HSI data compared to other modalities, namely RGB data and processed HSI data (e.g. tissue parameters like oxygenation), when performing semantic organ segmentation? According to a comprehensive validation study based on 506 HSI images from 20 pigs, annotated with a total of 19 classes, deep learning-based segmentation performance increases - consistently across modalities - with the spatial context of the input data. Unprocessed HSI data offers an advantage over RGB data or processed data from the camera provider, with the advantage increasing with decreasing size of the input to the neural network. Maximum performance (HSI applied to whole images) yielded a mean DSC of 0.90 ((standard deviation (SD)) 0.04), which is in the range of the inter-rater variability (DSC of 0.89 ((standard deviation (SD)) 0.07)). We conclude that HSI could become a powerful image modality for fully-automatic surgical scene understanding with many advantages over traditional imaging, including the ability to recover additional functional tissue information. Our code and pre-trained models are available at https://github.com/IMSY-DKFZ/htc.

Spectral organ fingerprints for machine learning-based intraoperative tissue classification with hyperspectral imaging in a porcine model.

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method's current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

Cell-type specific innervation of cortical pyramidal cells at their apical dendrites.

We investigated the synaptic innervation of apical dendrites of cortical pyramidal cells in a region between layers (L) 1 and 2 using 3-D electron microscopy applied to four cortical regions in mouse. We found the relative inhibitory input at the apical dendrite's main bifurcation to be more than 2-fold larger for L2 than L3 and L5 thick-tufted pyramidal cells. Towards the distal tuft dendrites in upper L1, the relative inhibitory input was at least about 2-fold larger for L5 pyramidal cells than for all others. Only L3 pyramidal cells showed homogeneous inhibitory input fraction. The inhibitory-to-excitatory synaptic ratio is thus specific for the types of pyramidal cells. Inhibitory axons preferentially innervated either L2 or L3/5 apical dendrites, but not both. These findings describe connectomic principles for the control of pyramidal cells at their apical dendrites and support differential computational properties of L2, L3 and subtypes of L5 pyramidal cells in cortex.