Pulmonary embolism

Pulmonary embolism (PE) is a blockage of an artery in the lungs by a substance that has moved from elsewhere in the body through the bloodstream (embolism). Symptoms of a PE may include shortness of breath, chest pain particularly upon breathing in, and coughing up blood. Symptoms of a blood clot in the leg may also be present, such as a red, warm, swollen, and painful leg. Signs of a PE include low blood oxygen levels, rapid breathing, rapid heart rate, and sometimes a mild fever. Severe cases can lead to passing out, abnormally low blood pressure, obstructive shock, and sudden death. PE usually results from a blood clot in the leg that travels to the lung. The risk of blood clots is increased by advanced age, cancer, prolonged bed rest and immobilization, smoking, stroke, long-haul travel over 4 hours, certain genetic conditions, estrogen-based medication, pregnancy, obesity, trauma or bone fracture, and after some types of surgery.

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LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

Bo Du, Rui Xu, Jiancheng Yang

Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vital for global information extraction. In computer vision tasks, non-local operations have been widely utilized, but the computational cost could be very high for 3D computed tomography (CT) images. To address this issue, we propose a long short slice-aware network (LSSANet) for the detection of pulmonary nodules. In particular, we develop a new non-local mechanism termed long short slice grouping (LSSG), which splits the compact non-local embeddings into a short-distance slice grouped one and a long-distance slice grouped counterpart. This not only reduces the computational burden, but also keeps long-range dependencies among any elements across slices and in the whole feature map. The proposed LSSG is easy-to-use and can be plugged into many pulmonary nodule detection networks. To verify the performance of LSSANet, we compare with several recently proposed and competitive detection approaches based on 2D/3D CNN. Promising evaluation results on the large-scale PN9 dataset demonstrate the effectiveness of our method. Code is at https:// github.com/Ruixxxx/LSSANet.

SPRINGER INTERNATIONAL PUBLISHING AG2022

What Makes for Automatic Reconstruction of Pulmonary Segments

Bo Du, Hongwei Li, Jiancheng Yang, Li Zhang

3D reconstruction of pulmonary segments plays an important role in surgical treatment planning of lung cancer, which facilitates preservation of pulmonary function and helps ensure low recurrence rates. However, automatic reconstruction of pulmonary segments remains unexplored in the era of deep learning. In this paper, we investigate what makes for automatic reconstruction of pulmonary segments. First and foremost, we formulate, clinically and geometrically, the anatomical definitions of pulmonary segments, and propose evaluation metrics adhering to these definitions. Second, we propose ImPulSe (Implicit Pulmonary Segment), a deep implicit surface model designed for pulmonary segment reconstruction. The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing. Compared with canonical segmentation methods, ImPulSe outputs continuous predictions of arbitrary resolutions with higher training efficiency and fewer parameters. Lastly, we experiment with different network inputs to analyze what matters in the task of pulmonary segment reconstruction. Our code is available at https://github.com/M3DV/ImPulSe.

SPRINGER INTERNATIONAL PUBLISHING AG2022

The Angiotensin-Converting Enzyme 2/Angiotensin-(1-7)/Mas receptor axis: A potential target for treating thrombotic diseases

Danielle Gomes Passos Silva, Nikolaos Stergiopulos

Despite many therapeutic advances leading to increasingly effective drug treatments, thrombotic events (such as ischaemic stroke, pulmonary embolism, deep venous thrombosis and acute myocardial infarction) still represent a major worldwide cause of morbidity and mortality. Remarkable effort has been made to identify new drug targets. There is growing evidence indicating that the recently described counter-regulator axis of the renin-angiotensin system (RAS), composed of Angiotensin-Converting Enzyme 2 (ACE2), Angiotensin-(1-7) and the Mas receptor, has protective effects against thrombosis. In addition, it could be considered as a promising target for treating or preventing this disease. In this narrative review, we focused on the recent findings of the role of the ACE2/Angiotensin-(1-7)/Mas axis on the haemostatic process and its therapeutic potential.

Schattauer Gmbh-Verlag Medizin Naturwissenschaften2012

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