Body Extraction Research Articles

An attention-based multiscale few-shot network for water body extraction from GF-2 satellite imagery

An attention-based multiscale few-shot network for water body extraction from GF-2 satellite imagery

070. Total Bowel Obstruction and Secondary Renal Impairment Following Bajakah Root Ingestion: Case Report

Background: Total bowel obstruction is a critical condition often caused by malignancies and adhesions or rare caused by foreign bodies. The bajakah root is a herbal medicine in Dayak community (Central Kalimantan), traditionally used to treat any diseases. This report presents a unique case of bowel obstruction due to bajakah root, and renal impairment as a complication. Case: A 62 years old female without any psychiatric disorders with history bajakah root consumtion complaint difficult defecation and green vomiting. Physical examination showed abdominal distention with collapsed rectal ampulla. Laboratory results revealed renal impairment. Imaging studies confirmed total bowel obstruction. Exploratory laparotomy identified ileoileal and ileo-omental adhesions with 0.5x0,5 cm ileum perforation 120 cm distal to the ligament of Treitz and a 2x2 cm wooden foreign body 10 cm distal from the perforation site. Surgical management included adhesiolysis, wedge excision of the ileum, foreign body extraction and anastomosis. Conclusion: Total bowel obstruction, renal impairment, bajakah, foreign body, foreign body extraction.

Comparative Analysis of Water Body Extraction Accuracy Based on Thresholding Method

Abstract. With the continuous development of remote sensing technology, various methods for land water body extraction based on satellite remote sensing have emerged. The thresholding method, as a commonly used image segmentation technique, possesses advantages such as high efficiency and wide applicability, making it widely employed in water body extraction research. In this thesis, utilizing SPOT4 imagery, we conducted experimental comparisons of water body extraction using the Iteration thresholding algorithm, Kittler-Illingworth (KI) thresholding algorithm, and Otsu thresholding algorithm. The experimental results demonstrate that all three thresholding methods exhibit good performance in water body extraction, with the Kittler-Illingworth (KI) thresholding algorithm achieving the highest relative accuracy.

An Unusual Case of Self-Inserted Rectal Foreign Body: Successful Management with Manual Extraction Under Spinal Anesthesia

Background: Rectal foreign bodies (RFBs) are an uncommon presentation to the emergency department (ED), often encountered by emergency medicine, surgery, and internal medicine specialists. Their management can be challenging, ranging from simple manual extraction to complex surgical interventions. Case presentation: A 19-year-old male presented to the ED with lower abdominal and anal pain. He admitted to self-inserting a plastic bottle into his rectum for autoerotic purposes. A physical examination revealed a palpable tubular mass in the rectosigmoid region, confirmed by a pelvic X-ray. The patient successfully underwent manual transanal extraction of the foreign body under spinal anesthesia, with no complications. Conclusion: This case highlights the importance of a thorough history and physical examination in patients presenting with RFBs. Prompt diagnosis and appropriate management, including manual extraction when feasible, can lead to successful outcomes.

Improving the UNet with channel attention mechanism for Taihu Lake water body recognition

Abstract Accurate recognition of surface water bodies is important for disaster management and resource planning. To explore a higher-precision water body recognition method, propose an improved channel attention mechanism UNet (ECA-UNet) water body recognition model based on Sentinel-2A remote sensing images. Utilizing ECA-UNet, UNet, PSPnet, and DeeplabV3, as well as the traditional normalized water body index (NDWI), recognize the water body in the Taihu Lake area. Through comparative analysis, the following main conclusions are drawn: (1) The recognition accuracy of the deep learning network model is better than the traditional NDWI method, among which the ECA-UNet network model has the highest accuracy of 98.67%, and the lowest recognition accuracy of NDWI is 89.06%. (2) The ECA-UNet network model can effectively extract water body recognition targets, has a better recognition effect on local features of water bodies, and can improve the water body extraction effect. The above results show that the improved channel attention mechanism ECA-UNet has the highest accuracy in water body recognition in Taihu Lake, which provides a reference for subsequent research and applications in this field.

Exploring the allergenic potential of sesame oleosins: Isolation and bioinformatics analysis

This study examined two oleosins of 17 kDa and 15 kDa isolated from Yuzhi white sesame seeds through oil body extraction. The allergens were identified as oleosin H1 (Ses i 4) and oleosin L (Ses i 5) using SDS-PAGE, dot blot analysis, and LC-MS/MS. PCR analysis revealed high sequence homology for the oleosin proteins in the sesame seeds. Utilizing AlphaFold2, bioinformatics tools, and protein-protein docking, the structure and function of these oleosins were analyzed. Ten potential B cell epitope peptides were predicted and mapped onto the α-helix and random coil-dominated oleosome membrane conformation. IgE binding simulations identified key epitopes, B3 (FLTSGAFGL) and B4 (KRGVQEGTLY) for oleosin H1, and B8 (GGFGVAALSV) and B9 (DQLESAKTKL) for oleosin L. Mutational analysis highlighted Glu135, Phe102, Tyr128, Tyr139, Gly136, and Gly132 in oleosin H1, and Leu120, Lys119, and Leu113 in oleosin L as critical residues for binding stability, providing insights into the sensitization mechanism of these epitopes. The integration of bioinformatics and immunoinformatics in this study has contributed to a deeper understanding of the allergy properties of sesame oleosins.

Automatic Water Body Extraction from SAR Images Based on MADF-Net

Water extraction from synthetic aperture radar (SAR) images has an important application value in wetland monitoring, flood monitoring, etc. However, it still faces the problems of low generalization, weak extraction ability of detailed information, and weak suppression of background noises. Therefore, a new framework, Multi-scale Attention Detailed Feature fusion Network (MADF-Net), is proposed in this paper. It comprises an encoder and a decoder. In the encoder, ResNet101 is used as a solid backbone network to capture four feature levels at different depths, and then the proposed Deep Pyramid Pool (DAPP) module is used to perform multi-scale pooling operations, which ensure that key water features can be captured even in complex backgrounds. In the decoder, a Channel Spatial Attention Module (CSAM) is proposed, which focuses on feature areas that are critical for the identification of water edges by fusing attention weights in channel and spatial dimensions. Finally, the high-level semantic information is effectively fused with the low-level edge features to achieve the final water detection results. In the experiment, Sentinel-1 SAR images of three scenes with different characteristics and scales of water body are used. The PA and IoU of water extraction by MADF-Net can reach 92.77% and 89.03%, respectively, which obviously outperform several other networks. MADF-Net carries out water extraction with high precision from SAR images with different backgrounds, which could also be used for the segmentation and classification of other tasks from SAR images.

A multi-view multi-label fast model for Auricularia cornea phenotype identification and classification

The identification and classification of various phenotypic features of Auricularia cornea fruit bodies are crucial for quality grading and breeding efforts. The phenotypic features of Auricularia cornea fruit bodies encompass size, number, shape, color, pigmentation, and damage. These phenotypic features are distributed across various views of the fruit bodies, making the task of achieving both rapid and accurate identification and classification challenging. This paper proposes a novel multi-view multi-label fast network that integrates two different views of the Auricularia cornea fruiting body, enabling rapid and precise identification and classification of six phenotypic features simultaneously. Initially, a multi-view feature extraction model based on partial convolution was constructed. This model incorporates channel attention mechanisms to achieve rapid phenotypic feature extraction of the Auricularia cornea fruiting body. Subsequently, an efficient multi-task classifier was designed, based on class-specific residual attention, to ensure accurate classification of phenotypic features. Finally, task weights were dynamically adjusted based on heteroscedastic uncertainty, reducing the training complexity of the multi-task classification. The proposed network achieved a classification accuracy of 94.66% and an inference speed of 11.9 ms on an image dataset of dried Auricularia cornea fruiting bodies with three views and six labels. The results demonstrate that the proposed network can efficiently and accurately identify and classify all phenotypic features of Auricularia cornea.

Extraction of River Water Bodies Based on ICESat-2 Photon Classification

The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies.

A New and Robust Index for Water Body Extraction from Sentinel-2 Imagery

Land surface water is a key part in the global ecosystem balance and hydrological cycle. Remote sensing has become an effective tool for its spatio-temporal monitoring. However, remote sensing results exemplified in so-called water indices are subject to several limitations. This paper proposes a new and effective water index called the Sentinel Multi-Band Water Index (SMBWI) to extract water bodies in complex environments from Sentinel-2 satellite imagery. Individual tests explore the effectiveness of the SMBWI in eliminating interference of various special interfering cover features. The Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) method and confusion matrix along with the derived accuracy evaluation indicators are used to provide a threshold reference when extracting water bodies and evaluate the accuracy of the water body extraction results, respectively. The SMBWI and eight other commonly used water indices are qualitatively and quantitatively compared through vision and accuracy evaluation indicators, respectively. Here, the SMBWI is proven to be the most effective at suppressing interference of buildings and their shadows, cultivated lands, vegetation, clouds and their shadows, alpine terrain with bare ground and glaciers when extracting water bodies. The overall accuracy in all tests was consistently greater than 96.5%. The SMBWI is proven to have a high ability to identify mixed pixels of water and non-water, with the lowest total error among nine water indices. Most notably, better results are obtained when extracting water bodies under interfering environments of cover features. Therefore, we propose that our novel and robust water index, the SMBWI, is ready to be used for mapping land surface water with high accuracy.

Comparative analysis of multispectral data between GF-1 WFV4 and GF-6 WFV sensors

ABSTRACT In recent years, China has launched a number of Gaofen (GF) series earth observation satellites. It is crucial to understand the relationship between the data of the GF series of satellite sensors for the selection of sensor images for scientific research. Taking the same-day transit image pairs of three regions as the study data, this paper compares the consistency of the Top of Atmosphere (TOA) reflectance of GF-1 WFV4 and GF-6 WFV sensors by using the TOA mean comparison method, and discusses the differences in water body and vegetation extraction. The results indicate that the satellite signal intensity in different land cover types and areas differs significantly. In the bare soil-dominated region, GF-1 WFV4 has a larger signal strength than GF-6 WFV, while in the vegetation-dominated region, it turns out to be just the opposite. The difference between the two sensors is mainly related to the difference in the spectral response function and the radiometric resolution of the two satellites. In addition, the determination coefficient (R2) of the corresponding bands of the two sensors is all greater than 0.90, indicating that the two sensors have strong linear correlation and good complementarity.

Identifying Temporal Change in Urban Water Bodies Using OpenStreetMap and Landsat Imagery: A Study of Hangzhou City

As one of the most important ecosystems, the water body is losing water during the rapid development of the city. To understand the impacts on water body change during the rapid urbanization period, this study combines data from the OpenStreetMap platform with Landsat 5/Thematic Mapper images to effectively and accurately identify small urban water bodies. The findings indicate that the trained U-net convolutional neural network (U-Net) water body extraction model and loss function combining Focal Loss and Dice Loss adopted in this study demonstrate high precision in identifying water bodies within the main urban area of Hangzhou, with an accuracy rate of 94.3%. Trends of decrease in water areas with a continuous increase in landscape fragmentation, particularly for the plain river network, were observed from 1985 to 2010, indicating a weaker connection between water bodies resulting from rapid urbanization. Large patches of water bodies, such as natural lakes and big rivers, located at divisions at the edge of the city are susceptible to disappearing during the rapid outward expansion. However, due to the limitations and strict control of development, water bodies, referring to as wetland, slender canals, and plain river networks, in the traditional center division of the city, are preserved well. Combined with the random forest classification method and the U-Net water body extraction model, land use changes from 1985 to 2010 are calculated. Reclamation along the Qiantang River accounts for the largest conversion area between water bodies and cultivated land, constituting more than 90% of the total land use change area, followed by the conversion of water bodies into construction land, particularly in the northeast of Xixi Wetland. Notably, the conversion of various land use types within Xixi Wetland into construction land plays a significant role in the rise of the carbon footprint.

Water body extraction from high spatial resolution remote sensing images based on enhanced U-Net and multi-scale information fusion

Employing deep learning techniques for the semantic segmentation of remote sensing images has emerged as a prevalent approach for acquiring information about water bodies. Yet, current models frequently fall short in accurately extracting water bodies from high-resolution remote sensing images, as these images often present intricate details of terrestrial objects and complex backgrounds. Vegetation, shadows, and other objects close to water boundaries have increased similarity to water bodies. Moreover, water bodies in high-resolution images have different boundary complexities, shapes, and sizes. This situation makes it somewhat challenging to accurately distinguish water bodies in high-resolution images. To overcome these difficulties, this paper presents a novel network model named EU-Net, specifically designed to extract water bodies from high-resolution remote sensing images. The proposed EU-Net model, with U-net as the backbone network, incorporates improved residual connections and attention mechanisms, and designs multi-scale dilated convolution and multi-scale feature fusion modules to enhance water body extraction performance in complex scenarios. Specifically, in the proposed model, improved residual connections are introduced to enable the learning of more complex features; the attention mechanism is employed to improve the model's discriminative ability by focusing on important channels and spatial areas. The implemented multi-scale dilated convolution technique enhances the model's receptive field while maintaining the same number of parameters. The designed multi-scale feature fusion module is capable of processing both small-scale details and large-scale structures in images, while simultaneously modeling the spatial context relationships of features at different scales. Experimental results validate the superior performance of EU-Net in accurately identifying water bodies from high-resolution remote sensing images, outperforming current models in terms of water extraction accuracy.

Generation of soluble, immunoreactive recombinant JEV E protein through a simplified inclusion body extraction and refolding approach in vitro

Japanese encephalitis (JE) is a mosquito-borne infectious disease caused by the Japanese encephalitis virus (JEV). There is currently no effective treatment for JE, and all approved Japanese encephalitis vaccine products originated from the JEV genotype III (GIII). In recent years, JEV genotype I (GI) has gradually replaced GIII as the dominant genotype, and a new symptom of peripheral nerve injury (PNI) caused by JEV NX1889 strain has attracted wide attention, in which JEV envelope (E) protein may be involved in early peripheral nerve injury. Here, the envelope (E) gene of JEV GIb, NX1889 strain was cloned into pET-32a (+) plasmid, and then the E protein was successfully expressed as the high-yield inclusion body form in Escherichia coli (E. coli) BL21 (DE3). Subsequently, we successfully developed a simplified approach to generate soluble JEV E protein by solubilization, and a stepwise dialysis refolding approach with the introduction of a redox system. The refolding recombinant E protein was identified by anti-JEV-E polyclonal antibody and the serum anti-E titer of mice immunized with refolded E protein was higher than 1: 409,600, which proves it has highly immunoreactive. Moreover, the recombinant E protein could recognize antibodies in samples of multiple species and types, such as mice, rabbits, and humans. The approach described above is economical and does not require complex eukaryotic expression systems to produce immunoreactive JEV E protein, which may be of great significance for the diagnosis and pathogenesis of JEV GI strain.

A dataset of surface water area with 12-day resolution in Lijiang River Basin from 2015 to 2022

Widely distributed in Guangxi, Karst landforms are characterized by heavy rainfall during the flood season, rapid hydrological dynamics, and notable spatiotemporal variations. A water is an important ecological foundation and natural resource, the timely and accurate acquisition of spatiotemporal changes in surface water bodies holds practical significance for the protection and management of water resources, rapid assessment of drought and flood disasters, and the realization of sustainable development across various economic industries. This dataset takes the Lijiang River Basin in Guilin, Guangxi as the research area. Addressing prevalent challenges such as infrequenct surface water monitoring, limited water body extraction methods due to sparse sample information, and low extraction accuracy in complex terrain areas, we combined the data from Sentinel-1 radar and Sentinel-2 in the study. Considering the different imaging principles of radar and optical sensors, we developed a vector classification method based on empirical thresholds for optical and other multi-source remote sensing data. The method can effectively eliminate interference of most ground object shadows, allowing for the construction of dataset of surface water area with 12-day resolution in Lijiang River Basin from 2015 to 2022. The dataset comprises a total of 196 periods of surface water body vector data. According to the results after verifying the spatial distribution and quantitative accuracy of the dataset, the dataset demonstrates its capability to accurately extract inlets and outlets of rivers, lakes and reservoirs, as well as small water bodies in complex terrain areas. The overall accuracy reaches 92.73%,with a Kappa coefficient of 0.85. This dataset can be used to support water resources protection and sustainable management of the ecological environment in the Lijiang River Basin. Additionally, it can provide reliable data for decision-making in areas such as emergency management, disaster prevention, water conservancy development, and economic development.

Difficulties in Extracting Dentures Stuck in The Esophagus

Foreign bodies in the esophagus are one of the common problems that ENT doctors often face. One of them is a foreign body with dentures in the esophagus. The diagnosis of a foreign body of dentures in the esophagus is established based on anamnesis, physical examination, radiological examination, and esophagoscopy. The act of esophagoscopy is performed as diagnostic and therapeutic. If not handled immediately and adequately, foreign bodies in the esophagus can cause complications such as esophageal perforation, blockage, and pressure of the foreign body into the airway and other complications. Patient readiness, availability of tools, and the presence of experienced experts are significant factors in overcoming this case. Prompt treatment using esophagoscopy is a safe and effective way to perform foreign body extraction in most cases. Thoracotomy to esophagectomy is performed in cases of foreign bodies in the esophagus that experience difficulties or failure in extracting by esophagoscopy. It was reported that two cases of foreign bodies with dentures in the esophagus were removed by thoracotomy accompanied by esophagectomy.

Flexible bronchoscopy for foreign body aspiration in children: A single-centre experience.

The technological evolution of bronchoscopy has led to the widespread adoption of flexible techniques and their use for both diagnostic and therapeutic purposes. Currently, there is an active debate regarding the comparative efficacy and safety of rigid vs flexible bronchoscopy in the treatment of foreign body aspiration. To evaluate our experience with tracheobronchial foreign body extraction using flexible bronchoscopy and provide a literature overview. This was a single-centre retrospective study. Twenty-four patients were enrolled between January 2017 and January 2023. Medical records of patients aged below 18 years who were admitted to authors' affiliated institution with a suspected diagnosis of foreign body aspiration were collected from hospital's database to Microsoft Excel 2019. Data were analysed using MedCalc Statistical Software. Patient ages varied from 9 months to 11 years. The median age was 23.5 months, 95% confidence interval (CI) 19.49-44.77. We observed age clustering in children with foreign body aspiration at our institution with three age subgroups: (1) 0-25 months; (2) 40-60 months; and (3) 120-140 months. We expectancy of an organic tracheobronchial foreign body was significantly higher in 0-25 months subgroup than that in older ones when subgroups 40-60 and 120-140 months were combined together (odds ratio = 10.0, 95%CI: 1.44-29.26, P = 0.0197). Successful foreign body extraction was performed in all cases. Conversion to a rigid bronchoscope was not required in any of the cases. No major complications (massive bleeding, tracheobronchial tree perforation, or asphyxia) were observed. Flexible bronchoscopy is an effective and safe method for tracheobronchial foreign body extraction in children.

Disposition kinetics and tissue residues of tilmicosin following intravenous, subcutaneous, single and multiple oral dosing in geese (Anser anser domesticus).

Tilmicosin (TMC), a semi-synthetic macrolide antibiotic, is widely used in veterinary medicine due to its broad-spectrum, bacteriostatic properties. Frequently administered in various birds species, it is likely used off-label in geese as well. The study sought to investigate TMC's pharmacokinetics, tissue residues, in geese through invivo experiments. The study involved longitudinal open studies on 15 healthy adult males, with three phases separated by one-month washout periods. Geese were administered TMC through intravenous (IV, 5 mg/kg), subcutaneous (SC, 10 mg/kg), and oral (PO, 25 mg/kg for five consecutive days) routes, with blood samples drawn at specific intervals. Tissue samples were also collected for subsequent analysis at pre-assigned times. TMC in goose plasma was quantified by a fully validated HPLC method. Plasma concentrations were quantified up to 4 hr for the PO and IV routes, and up to 10 hr in the SC route. Significant variations in bioavailability were observed between SC (87%) and PO (4%) routes. The body extraction ratio was low at 0.03, suggesting minimal ability of the liver and kidneys to eliminate TMC. Multiple oral doses showed no plasma accumulation, but tissue data revealed extensive distribution and prolonged residence, up to 120 h, suggesting a sustained therapeutic effect despite the brief plasma half-life. Regarding the multiple PO doses, provisional withdrawal times of 6, 7.5, and 8 days were suggested for the liver, muscles, and kidneys, respectively, according to the MRL set for these matrices in chickens by EMA. In conclusion, while the practical oral administration is discouraged at the population level, SC administration of TMC may be appropriate for geese, albeit impractical for flock therapy.

The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

Right Middle Lobectomy on Metallic Foreign Body Using the Uniportal Video Assisted Thoracoscopic Surgery: A First Experience in the Democratic Republic of Congo

The removal of metallic foreign bodies in the lower airways, a source of recurrent pneumonia, is not currently performed in the Congolese population using video-assisted thoracoscopy surgery (VATS) under unipulmonary ventilation. Multiple failures of rigid and/or flexible bronchoscopy are likely to be a cause of morbidity and mortality. To the best of our knowledge, we report a first experience in DRC, of right middle lobectomy on metallic foreign body by VATS in a 14-year-old girl after two years of accidental inhalation of a metallic key, in the face of two bronchoscopy failures. A partial transverse bronchotomy successfully performed under VATS allowed extraction of the foreign body followed by bronchial reconstruction with resection of the right middle lung lobe. The aim of the multidisciplinary team was to determine whether lobectomy by VATS would result in shorter hospital stays, fewer complications and faster recovery than lobectomy by thoracotomy, to review the key stages of case management using an unusual approach in the region, and to draw a conclusion based on this first experience.