Quantifying Data Augmentation for LiDAR based 3D Object Detection

In this work, we shed light on different data augmentation techniques commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection. We, therefore, utilize a state of the art voxel-based 3D Object Detection pipeline called PointPillars and carry out our experiments on the well established KITTI dataset. We investigate a variety of global and local augmentation techniques, where global augmentation techniques are applied to the entire point cloud of a scene and local augmentation techniques are only applied to points belonging to individual objects in the scene. Our findings show that both types of data augmentation can lead to performance increases, but it also turns out, that some augmentation techniques, such as individual object translation, for example, can be counterproductive and can hurt overall performance. We show that when we apply our findings to the data augmentation policy of PointPillars we can easily increase its performance by up to 2%. In order to provide reproducibility, our code will be publicly available at www.trace.ethz.ch/3D_Object_Detection

Semantic Understanding of Foggy Scenes with Purely Synthetic Data

This work addresses the problem of semantic scene understanding under foggy road conditions. Although marked progress has been made in semantic scene understanding over the recent years, it is mainly concentrated on clear weather outdoor scenes. Extending semantic segmentation methods to adverse weather conditions like fog is crucially important for outdoor applications such as self-driving cars. In this paper, we propose a novel method, which uses purely synthetic data to improve the performance on unseen real-world foggy scenes captured in the streets of Zurich and its surroundings. Our results highlight the potential and power of photo-realistic synthetic images for training and especially fine-tuning deep neural nets. Our contributions are threefold, 1) we created a purely synthetic, high-quality foggy dataset of 25,000 unique outdoor scenes, that we call Foggy Synscapes and plan to release publicly 2) we show that with this data we outperform previous approaches on real-world foggy test data 3) we show that a combination of our data and previously used data can even further improve the performance on real-world foggy data.Comment: independent class IoU scores corrected for BiSiNet architectur

LiDAR Snowfall Simulation for Robust 3D Object Detection

3D object detection is a central task for applications such as autonomous driving, in which the system needs to localize and classify surrounding traffic agents, even in the presence of adverse weather. In this paper, we address the problem of LiDAR-based 3D object detection under snowfall. Due to the difficulty of collecting and annotating training data in this setting, we propose a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds. Our method samples snow particles in 2D space for each LiDAR line and uses the induced geometry to modify the measurement for each LiDAR beam accordingly. Moreover, as snowfall often causes wetness on the ground, we also simulate ground wetness on LiDAR point clouds. We use our simulation to generate partially synthetic snowy LiDAR data and leverage these data for training 3D object detection models that are robust to snowfall. We conduct an extensive evaluation using several state-of-the-art 3D object detection methods and show that our simulation consistently yields significant performance gains on the real snowy STF dataset compared to clear-weather baselines and competing simulation approaches, while not sacrificing performance in clear weather. Our code is available at www.github.com/SysCV/LiDAR_snow_sim.Comment: Oral at CVPR 202

Radiation exposure of adrenal vein sampling: a German Multicenter Study

Objective: Adrenal vein sampling (AVS) represents the current diagnostic standard for subtype differentiation in primary aldosteronism (PA). However, AVS has its drawbacks. It is invasive, expensive, requires an experienced interventional radiologist and comes with radiation exposure. However, exact radiation exposure of patients undergoing AVS has never been examined. Design and methods: We retrospectively analyzed radiation exposure of 656 AVS performed between 1999 and 2017 at four university hospitals. The primary outcomes were dose area product (DAP) and fluoroscopy time (FT). Consecutively the effective dose (ED) was approximately calculated. Results: Median DAP was found to be 32.5Gy*cm(2) (0.3-3181) and FT 18 min (0.3-184). The calculated ED was 6.4 mSv (0.1-636). Remarkably, values between participating centers highly varied: Median DAP ranged from 16 to 147 Gy*cm(2), FT from 16 to 27 min, and ED from 3.2 to 29 mSv. As main reason for this variation, differences regarding AVS protocols between centers could be identified, such as number of sampling locations, frames per second and the use of digital subtraction angiographies. Conclusion: This first systematic assessment of radiation exposure in AVS not only shows fairly high values for patients, but also states notable differences among the centers. Thus, we not only recommend taking into account the risk of radiation exposure, when referring patients to undergo AVS, but also to establish improved standard operating procedures to prevent unnecessary radiation exposure

Impact of tumor burden on normal organ distribution in patients imaged with CXCR4-targeted [68Ga]Ga-PentixaFor PET/CT

BACKGROUND: CXCR4-directed positron emission tomography/computed tomography (PET/CT) has been used as a diagnostic tool in patients with solid tumors. We aimed to determine a potential correlation between tumor burden and radiotracer accumulation in normal organs. METHODS: Ninety patients with histologically proven solid cancers underwent CXCR4-targeted [(68)Ga]Ga-PentixaFor PET/CT. Volumes of interest (VOIs) were placed in normal organs (heart, liver, spleen, bone marrow, and kidneys) and tumor lesions. Mean standardized uptake values (SUV(mean)) for normal organs were determined. For CXCR4-positive tumor burden, maximum SUV (SUV(max)), tumor volume (TV), and fractional tumor activity (FTA, defined as SUV(mean) x TV), were calculated. We used a Spearman's rank correlation coefficient (ρ) to derive correlative indices between normal organ uptake and tumor burden. RESULTS: Median SUV(mean) in unaffected organs was 5.2 for the spleen (range, 2.44 – 10.55), 3.27 for the kidneys (range, 1.52 – 17.4), followed by bone marrow (1.76, range, 0.84 – 3.98), heart (1.66, range, 0.88 – 2.89), and liver (1.28, range, 0.73 – 2.45). No significant correlation between SUV(max) in tumor lesions (ρ ≤ 0.189, P ≥ 0.07), TV (ρ ≥ -0.204, P ≥ 0.06) or FTA (ρ ≥ -0.142, P ≥ 0.18) with the investigated organs was found. CONCLUSIONS: In patients with solid tumors imaged with [(68)Ga]Ga-PentixaFor PET/CT, no relevant tumor sink effect was noted. This observation may be of relevance for therapies with radioactive and non-radioactive CXCR4-directed drugs, as with increasing tumor burden, the dose to normal organs may remain unchanged

Urine steroid metabolomics as a diagnostic tool in primary aldosteronism

Primary aldosteronism (PA) causes 5-10% of hypertension cases, but only a minority of patients are currently diagnosed and treated because of a complex, stepwise, and partly invasive workup. We tested the performance of urine steroid metabolomics, the computational analysis of 24-hour urine steroid metabolome data by machine learning, for the identification and subtyping of PA. Mass spectrometry-based multi-steroid profiling was used to quantify the excretion of 34 steroid metabolites in 24-hour urine samples from 158 adults with PA (88 with unilateral PA [UPA] due to aldosterone-producing adenomas [APAs]; 70 with bilateral PA [BPA]) and 65 sex- and age-matched healthy controls. All APAs were resected and underwent targeted gene sequencing to detect somatic mutations associated with UPA. Patients with PA had increased urinary metabolite excretion of mineralocorticoids, glucocorticoids, and glucocorticoid precursors. Urine steroid metabolomics identified patients with PA with high accuracy, both when applied to all 34 or only the three most discriminative steroid metabolites (average areas under the receiver-operating characteristics curve [AUCs-ROC] 0.95-0.97). Whilst machine learning was suboptimal in differentiating UPA from BPA (average AUCs-ROC 0.65-0.73), it readily identified APA cases harbouring somatic KCNJ5 mutations (average AUCs-ROC 0.79-85). These patients showed a distinctly increased urine excretion of the hybrid steroid 18-hydroxycortisol and its metabolite 18-oxo-tetrahydrocortisol, the latter identified by machine learning as by far the most discriminative steroid. In conclusion, urine steroid metabolomics is a non-invasive candidate test for the accurate identification of PA cases and KCNJ5-mutated APAs.</p

Targeting CXCR4 (CXC Chemokine Receptor Type 4) for Molecular Imaging of Aldosterone-Producing Adenoma

Primary aldosteronism is the most frequent cause of secondary hypertension and is associated with increased morbidity and mortality compared with hypertensive controls. The central diagnostic challenge is the differentiation between bilateral and unilateral disease, which determines treatment options. Bilateral adrenal venous sampling, currently recommended for differential diagnosis, is an invasive procedure with several drawbacks, making it desirable to develop novel noninvasive diagnostic tools. When investigating the expression pattern of chemokine receptors by quantitative real-time polymerase chain reaction and immunohistochemistry, we observed high expression of CXCR4 (CXC chemokine receptor type 4) in aldosterone-producing tissue in normal adrenals, adjacent adrenal cortex from adrenocortical adenomas, and in aldosterone-producing adenomas (APA), correlating strongly with the expression of CYP11B2 (aldosterone synthase). In contrast, CXCR4 was not detected in the majority of nonfunctioning adenomas that are frequently found coincidently. The specific CXCR4 ligand 68Ga-pentixafor has recently been established as radiotracer for molecular imaging of CXCR4 expression and showed strong and specific binding to cryosections of APAs in our study. We further investigated 9 patients with primary aldosteronism because of APA by 68Ga-pentixafor-positron emission tomography. The tracer uptake was significantly higher on the side of increased adrenocortical aldosterone secretion in patients with APAs compared with patients investigated by 68Ga-pentixafor-positron emission tomography for other causes. Molecular imaging of aldosterone-producing tissue by a CXCR4-specific ligand may, therefore, be a highly promising tool for noninvasive characterization of patients with APAs