Liver Segments Research Articles

Left-Sided Inferior Vena Cava: A Retrospective Study and Literature Review

ObjectiveThe primary goal of this research is to delve into the clinical and pathological facets of the left-sided inferior vena cava (IVC), and to catalogue and condense its radiological and clinical attributes, thereby furnishing valuable references for pertinent clinical diagnosis and therapeutic procedures. MethodsWe collated and scrutinized the general clinical features, radiological characteristics, and diagnostic and therapeutic strategies of 30 patients diagnosed with left-sided IVC in our hospital from July 2014 through February 2024. ResultsA majority of patients were asymptomatic and were only identified during diagnostic procedures for other ailments. CT scans revealed anomalies in the anatomical configuration of the left-sided IVC. The radiological presentations primarily showcased the right common iliac vein traversing the lumbar vertebrae to amalgamate with the left common iliac vein, forming the IVC. The IVC ascended on the left side of the abdominal aorta, accepted the left renal vein, and then transitioned to the right side of the abdominal aorta. In three instances, the IVC was witnessed ascending on the left side of the abdominal aorta, permeating through the diaphragm, converging with the azygos vein and abdominal aorta, and making its way into the right atrium. In these cases, the hepatic segment of the IVC was missing, and there was an absence of the IVC inferior to the hepatic vein, a condition we refer to as complete left-sided IVC. ConclusionLeft-sided IVC is predominantly asymptomatic but carries significant anatomical implications during abdominal, retroperitoneal surgeries, and vascular interventions. Precise identification and management of this anomaly can mitigate surgical risks and enhance patient prognosis.

Cystic plate approach combined with ICG fluorescence in laparoscopic anatomical hepatectomy.

The in-depth understanding of the fine anatomy of the liver has promoted the development of modern liver surgery. With the rapid popularity of laparoscopic hepatectomy, the membrane structure of the liver and its ability to dissect the intrahepatic and extrahepatic vascular system more conveniently and accurately has been gradually emphasized. Exploring the value of extrahepatic sheath dissection of the hepatic pedicle in minimally invasive anatomical hepatectomy with cystic plate approach. This study aims to assess the benefits of integrating the cystic plate approach with real-time guided laparoscopic anatomical hepatectomy, in comparison with conventional laparoscopic anatomical hepatectomy. Based on the theory of cystic plate and hepatic portal plate, the authors have pioneered the fluorescence real-time guided cystic plate approach in hepatectomy. The article focuses on the anatomical knowledge and technical difficulties of anatomical hepatectomy with fluoroscopic laparoscopic cystic plate approach and explores the safety and practicality of the cystic plate approach in laparoscopic anatomical hepatectomy. Additionally, a retrospective cohort study was also conducted to compare the operation time, intraoperative blood loss, and postoperative complications between the cystic plate approach and the conventional approach during fluoroscopic laparoscopic hepatectomy. A total of 38 patients who met the inclusion criteria underwent laparoscopic hepatectomy between January 2019 and November 2022. No significant disadvantages were found in terms of operation time and intraoperative blood loss during the surgeries. Furthermore, the postoperative indications, including liver function indexes on the first postoperative day, WBC, and the postoperative hospital stay, were also not affected, thus proving the safety of the cystic approach. Importantly, through the cystic plate approach, the target liver pedicle was fully freed, and then the segments to be resected were precisely marked by positive or negative staining, followed by hepatectomy under real-time fluoroscopic guidance. This approach is extremely advantageous in anatomical liver segment resections, especially in right posterior lobe or hemi-hepatectomy, without increasing intraoperative bleeding or postoperative complication rates. This technique allows for easy and safe freeing of the target liver pedicle using membrane structures, and also allows for precise anatomical hepatectomy in combination with real-time fluoroscopic laparoscopic navigation.

Instrumentless liver suspension for liver retraction in robotic pancreatoduodenectomy.

The popularity of robotic pancreatoduodenectomy (RPD) is increasing, yet it remains a complex procedure. Outcomes are influenced by various factors, including patient-specific variables, disease characteristics, and surgical technique. Numerous and intricate details contribute to the technical success of RPD. In this study, our focus is on achieving effective and "gentle" liver retraction. The use of liver retractors has been associated with the risk of retractor-related liver injury (RRLI), which can have serious consequences. Here, we introduce a refined technique for instrumentless liver retraction in RPD, developed progressively through a series of over 300 procedures. The core concept of this technique involves suspending the liver to the diaphragmatic dome. This is accomplished by securing the round ligament to the anterior abdominal wall using transparietal sutures and attaching the fundus of the gallbladder and the anterior margin of liver segment number 3 to the diaphragm. Our consecutive series of over 300 RPDs demonstrates the feasibility and safety of this approach, with no clinically relevant RRLI observed. Instrumentless liver retraction offers a valuable refinement in RPD, streamlining the procedure while reducing potential complications associated with dedicated retractors.

Vascular liver segmentation: a narrative review on methods and new insights brought by artificial intelligence.

Liver vessel segmentation from routinely performed medical imaging is a useful tool for diagnosis, treatment planning and delivery, and prognosis evaluation for many diseases, particularly liver cancer. A precise representation of liver anatomy is crucial to define the extent of the disease and, when suitable, the consequent resective or ablative procedure, in order to guarantee a radical treatment without sacrificing an excessive volume of healthy liver. Once mainly performed manually, with notable cost in terms of time and human energies, vessel segmentation is currently realized through the application of artificial intelligence (AI), which has gained increased interest and development of the field. Many different AI-driven models adopted for this aim have been described and can be grouped into different categories: thresholding methods, edge- and region-based methods, model-based methods, and machine learning models. The latter includes neural network and deep learning models that now represent the principal algorithms exploited for vessel segmentation. The present narrative review describes how liver vessel segmentation can be realized through AI models, with a summary of model results in terms of accuracy, and an overview on the future progress of this topic.

Central Liver Segments Resections vs Extended Hepatectomies in Children: Single-center Experience

BackgroundCentral liver segments resection (CLSR) still is not widely used in pediatric surgery due to its technical difficulty, whereas this procedure is widely spread as a parenchyma sparing approach of centrally located liver tumors in adults. The aim of this study is to analyze the outcomes of CLSR in comparison with extended hepatectomy (EH) in children with different liver tumors. MethodsA single-center retrospective analysis of patients who received CLSR (n = 14) and EH (n = 44) from June 2017 to December 2023 was applied. Patient's characteristics, preoperative, intra- and postoperative data were compared between 2 groups. ResultsPreoperative CT-volumetry showed that future liver remnant volume was higher in CLSR group compared to EH (FLR-V; (54 ± 29 (40–91) % vs 40 ± 12 (17–73) %, p = 0.016). The intraoperative blood loss (200 [90–1150] (20–3000) ml vs 100 [30–275] (10–9000) ml, p = 0.088) and transfusion volume (310 [85–590] (0–1860) ml vs 150 [0–310] (0–4770) ml, p = 0.484) were similar in both groups, while operation time was longer in CLSR group (420 [320–595] (145–785) min vs 280 [203–390] (125–710) min), p = 0.011). There was no difference in biliary leakage (3 (21.4 %) vs 12 (27.3 %); p = 0.479), other complications (4 (28.6 %) vs 5 (11.4 %), p = 0.198) and complications ≥ IIIb by Clavien-Dindo (2 (14.3 %) vs 8 (18.2 %), p = 0.385) postoperatively. ConclusionCLSRs allow to preserve more healthy liver parenchyma compared to EH with similar intraoperative and postoperative outcomes. «Extended mesohepatectomy» allows to achieve R0 resection when central liver tumor extends on the left lateral and/or right posterior section. Type of StudyRetrospective Comparative Study (Level of Evidence III).

P.408: Left lateral segment liver transplantation in the cirrhotic setting with hepatocellular carcinoma: A case report.

P.408: Left lateral segment liver transplantation in the cirrhotic setting with hepatocellular carcinoma: A case report.

DESMPA-Deep CNN: Double Exponential Smoothing Marine Predator Algorithm-Based Deep CNN for Liver Tumor Segmentation and Classification

Nowadays, liver cancers are well-known cancers attacking the human body. The larger segment of liver carcinomas is highly affected by alcohol-associated hepatitis as well as cirrhosis circumstances. In order to increase the survival rate of humans, premature and early diagnosis of liver cancer is very vital. Currently, discriminating the tumor and liver segments from medical imaging with the help of entirely automated computer-aided software is a highly difficult task, because the liver tumor may differ from one human being to another human being. Thus, this paper endeavors to present a new liver tumor segmentation and classification approach by incorporating an optimization-enabled deep learning model. Here, the adopted model comprises four stages, pre-processing, liver segmentation, liver tumor detection, and classification. To begin with, the collected images are given to the pre-processing stage using the African vulture’s optimization-based adaptive histogram equalization (AVOAHE). The integration of the African vulture’s optimization (AVO) concept in adaptive histogram equalization is named AVOAHE. After the image pre-processing phase, the liver region is segmented by utilizing kernel-based fuzzy C-means clustering. Then, the detection of liver tumor is performed using the Swin-Unet. Finally, tumor detection is done by utilizing the deep convolutional neural network (Deep CNN). Moreover, a hybrid optimization approach, called double exponential smoothing (DES)-marine predator algorithm (MPA), is developed by combining DES and MPA, which is used to train Deep CNN and serves as an enhancement. Finally, the experimental analysis has shown that the adopted technique has achieved better results in terms of accuracy, specificity, sensitivity, and precision with values of 0.957, 0.969, 0.941 and 0.915, respectively.

Radiation-induced liver disease mimicking liver metastasis after low-dose hepatic irradiation during radiotherapy for gastric mucosa-assisted lymphoid tissue lymphoma: A case report.

Radiation-induced liver disease (RILD) is an established complication of hepatic irradiation that is typically reported in patients receiving high-dose radiotherapy for hepatocellular carcinoma or liver metastases. However, RILD can also occur after unintentional low-dose liver exposure during radiotherapy for other gastrointestinal malignancies when careful precautions are not taken. We report the case of a 44-year-old woman with gastric mucosa-associated lymphoid tissue lymphoma who underwent salvage radiotherapy administered to the entire stomach. One month after completing this radiotherapy, computed tomography and magnetic resonance imaging of the patient's abdomen revealed a 4 cm lesion in the left lateral liver segment, suggestive of metastasis. An ultrasound-guided biopsy was performed, and the histopathological findings were consistent with those of RILD. Conservative management was pursued with close monitoring of liver function tests. The patient's imaging findings and liver enzyme levels normalized approximately 3 months after the initial diagnosis. This case highlights the importance of considering RILD in the differential diagnosis of new hepatic lesions detected after radiotherapy, even in patients with low-dose liver exposure within generally acceptable limits. Careful correlation with the radiotherapy plan is crucial to avoid misdiagnosing RILD as metastatic disease and to guide appropriate management.

Laparoscopic right upper transversal hepatectomy: Strategies for right hepatic vein resection and reconstruction

ObjectiveThe right upper transversal hepatectomy (RUTH) is considered a complex technique of parenchymal-sparing hepatectomies. The intraoperative management of the right hepatic vein (RHV) is still controversial because it may cause obstruction of outflow in the remnant hepatic segment. The aim of this study is to present our experience of laparoscopic RUTH and the strategy of RHV resection and reconstruction in different settings. MethodsFive patients who underwent laparoscopic RUTH for liver tumor were enrolled retrospectively. Clinical and pathological features of the patients, peri-operative treatment, as well as short- and long-term outcomes were collected for analysis. ResultsLaparoscopic RUTH was successfully performed in all five patients. Two individuals underwent RUTH while preserving RHV. Among the remaining patients who underwent RUTH with RHV resection, one patient underwent RHV reconstruction but the others did not. Immediate or long-term venous related complications did not occurred in a median follow-up period of nine months. ConclusionsLaparoscopic RUTH surgery for tumors in the right upper region of the liver is safe and feasible. The strategic workflow we proposed for the resection and reconstruction of the RHV offers a reliable method for preserving liver parenchyma and reducing the risk of postoperative liver failure.

A Review of Advancements and Challenges in Liver Segmentation.

Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks.

Deep learning for automated boundary detection and segmentation in organ donation photography

Abstract Objectives Medical photography is ubiquitous and plays an increasingly important role in the fields of medicine and surgery. Any assessment of these photographs by computer vision algorithms requires first that the area of interest can accurately be delineated from the background. We aimed to develop deep learning segmentation models for kidney and liver organ donation photographs where accurate automated segmentation has not yet been described. Methods Two novel deep learning models (Detectron2 and YoloV8) were developed using transfer learning and compared against existing tools for background removal (macBGRemoval, remBGisnet, remBGu2net). Anonymised photograph datasets comprised training/internal validation sets (821 kidney and 400 liver images) and external validation sets (203 kidney and 208 liver images). Each image had two segmentation labels: whole organ and clear view (parenchyma only). Intersection over Union (IoU) was the primary outcome, as the recommended metric for assessing segmentation performance. Results In whole kidney segmentation, Detectron2 and YoloV8 outperformed other models with internal validation IoU of 0.93 and 0.94, and external validation IoU of 0.92 and 0.94, respectively. Other methods – macBGRemoval, remBGisnet and remBGu2net – scored lower, with highest internal validation IoU at 0.54 and external validation at 0.59. Similar results were observed in liver segmentation, where Detectron2 and YoloV8 both showed internal validation IoU of 0.97 and external validation of 0.92 and 0.91, respectively. The other models showed a maximum internal validation and external validation IoU of 0.89 and 0.59 respectively. All image segmentation tasks with Detectron2 and YoloV8 completed within 0.13–1.5 s per image. Conclusions Accurate, rapid and automated image segmentation in the context of surgical photography is possible with open-source deep-learning software. These outperform existing methods and could impact the field of surgery, enabling similar advancements seen in other areas of medical computer vision.

Research on tumors segmentation based on image enhancement method

One of the most effective ways to treat liver cancer is to perform precise liver resection surgery, the key step of which includes precise digital image segmentation of the liver and its tumor. However, traditional liver parenchymal segmentation techniques often face several challenges in performing liver segmentation: lack of precision, slow processing speed, and computational burden. These shortcomings limit the efficiency of surgical planning and execution. In this work, the model initially describes in detail a new image enhancement algorithm that enhances the key features of an image by adaptively adjusting the contrast and brightness of the image. Then, a deep learning-based segmentation network was introduced, which was specially trained on the enhanced images to optimize the detection accuracy of tumor regions. In addition, multi-scale analysis techniques have been incorporated into the study, allowing the model to analyze images at different resolutions to capture more nuanced tumor features. In the presentation of the experimental results, the study used the 3Dircadb dataset to test the effectiveness of the proposed method. The experimental results show that compared with the traditional image segmentation method, the new method using image enhancement technology has significantly improved the accuracy and recall rate of tumor identification.

Enhancing liver surgery and transplantation: the role of 3D printing and virtual reality

This review explores the significant advancements in liver surgery and transplantation, particularly focusing on the integration of 3D printing and virtual reality (VR) technologies. The core objective is to enhance preoperative planning, simulation, and intraoperative navigation. The review discusses several studies that underscore the accuracy and utility of 3D printed models derived from medical imaging, which are instrumental in identifying small liver lesions, improving surgical education, and facilitating patient comprehension. Additionally, the role of VR in surgical simulation is examined, highlighting its superiority in tumor identification and its potential in training systems. While clinical outcomes data suggest a need for further randomized trials to establish the impact on surgical efficiency and recovery, the review also touches upon the promising future of augmented reality (AR) for intraoperative guidance and liver segment identification, with prospects of artificial intelligence (AI) integration. The conclusion underscores the importance of continued clinical evidence and technological advancements for wider adoption in liver surgery and transplantation.

Modified laparoscopic transthoracic hepatectomy for upper liver segments: Technique, indications, and early outcomes.

Transthoracic access emerges as an innovative approach to reach lesions in the upper hepatic segments, especially in patients with prior surgeries. This study evaluates transthoracic access for these resections through a retrospective single-center analysis of demographic data, surgical techniques, and postoperative outcomes of 353 liver surgeries, revealing promising results with minimal complications. Transthoracic access and pneumoperitoneum establishment via the transthoracic route, combined with intercostal trocar insertion, offer a viable alternative for minimally invasive liver surgeries.

Assessing the performance of AI-assisted technicians in liver segmentation, Couinaud division, and lesion detection: a pilot study

BackgroundIn patients with primary and secondary liver cancer, the number and sizes of lesions, their locations within the Couinaud segments, and the volume and health status of the future liver remnant are key for informing treatment planning. Currently this is performed manually, generally by trained radiologists, who are seeing an inexorable growth in their workload. Integrating artificial intelligence (AI) and non-radiologist personnel into the workflow potentially addresses the increasing workload without sacrificing accuracy. This study evaluated the accuracy of non-radiologist technicians in liver cancer imaging compared with radiologists, both assisted by AI.MethodsNon-contrast T1-weighted MRI data from 18 colorectal liver metastasis patients were analyzed using an AI-enabled decision support tool that enables non-radiology trained technicians to perform key liver measurements. Three non-radiologist, experienced operators and three radiologists performed whole liver segmentation, Couinaud segment segmentation, and the detection and measurements of lesions aided by AI-generated delineations. Agreement between radiologists and non-radiologists was assessed using the intraclass correlation coefficient (ICC). Two additional radiologists adjudicated any lesion detection discrepancies.ResultsWhole liver volume showed high levels of agreement between the non-radiologist and radiologist groups (ICC = 0.99). The Couinaud segment volumetry ICC range was 0.77–0.96. Both groups identified the same 41 lesions. As well, the non-radiologist group identified seven more structures which were also confirmed as lesions by the adjudicators. Lesion diameter categorization agreement was 90%, Couinaud localization 91.9%. Within-group variability was comparable for lesion measurements.ConclusionWith AI assistance, non-radiologist experienced operators showed good agreement with radiologists for quantifying whole liver volume, Couinaud segment volume, and the detection and measurement of lesions in patients with known liver cancer. This AI-assisted non-radiologist approach has potential to reduce the stress on radiologists without compromising accuracy.Graphical abstract

Autofluorescence-based tissue characterization enhances clinical prospects of light-sheet-microscopy

Light sheet fluorescence microscopy (LSFM) is a transformative imaging method that enables the visualization of non-dissected specimen in real-time 3D. Optical clearing of tissues is essential for LSFM, typically employing toxic solvents. Here, we test the applicability of a non-hazardous alternative, ethyl cinnamate (ECi). We comprehensively characterized autofluorescence (AF) spectra in diverse murine tissues—ocular globe, knee, and liver—employing LSFM under various excitation wavelengths (405–785 nm) to test the feasibility of unstained samples for diagnostic purposes, in particular regarding percutaneous biopsies, as they constitute to most harvested type of tissue sample in clinical routine. Ocular globe structures were best discerned with 640 nm excitation. Knee tissue showed complex variation in AF spectra variation influenced by tissue depth and structure. Liver exhibited a unique AF pattern, likely linked to vasculature. Hepatic tissue samples were used to demonstrate the compatibility of our protocol for antibody staining. Furthermore, we employed machine learning to augment raw images and segment liver structures based on AF spectra. Radiologists rated representative samples transferred to the clinical assessment software. Learning-generated images scored highest in quality. Additionally, we investigated an actual murine biopsy. Our study pioneers the application of AF spectra for tissue characterization and diagnostic potential of optically cleared unstained percutaneous biopsies, contributing to the clinical translation of LSFM.

Translating contrast enhanced computed tomography images to liver radioembolization dose distribution for more comprehensively indicating patients

Objective. Contrast-enhanced computed tomography (CECT) is commonly used in the pre-treatment evaluation of liver Y-90 radioembolization feasibility. CECT provides detailed imaging of the liver and surrounding structures, allowing healthcare providers to assess the size, location, and characteristics of liver tumors prior to the treatment. Here we propose a method for translating CECT images to an expected dose distribution for tumor(s) and normal liver tissue. Approach. A pre-procedure CECT is used to obtain an iodine arterial-phase distribution by subtracting the non-contrast CT from the late arterial phase. The liver segments surrounding the targeted tumor are selected using Couinaud’s method. The resolution of the resulting images is then degraded to match the resolution of the positron emission tomography (PET) images, which can image the Y-90 activity distribution post-treatment. The resulting images are then used in the same way as PET images to compute doses using the local deposition method. CECT images from three patients were used to test this method retrospectively and were compared with Y-90 PET-based dose distributions through dose volume histograms. Main results. Results show a concordance between predicted and delivered Y-90 dose distributions with less than 10% difference in terms of mean dose, for doses greater than 10% of the 98th percentile (D2%). Significance. CECT-derived predictions of Y-90 radioembolization dose distributions seem promising as a supplementary tool for physicians when assessing treatment feasibility. This dosimetry prediction method could provide a more comprehensive pre-treatment evaluation—offering greater insights than a basic assessment of tumor opacification on CT images.

A case of intrahepatic mass-forming portal biliopathy mimicking intrahepatic cholangiocarcinoma.

A 75-year-old man was referred to our department because of an enlarging intrahepatic mass detected on magnetic resonance imaging (MRI) follow-up for another disease. MRI showed hypointensity on T1-weighted imaging and hyperintensity on T2-weighted imaging in liver segment 4. Abdominal plain computed tomography (CT) indicated a low-density lesion with an unclear boundary, measuring approximately 4cm × 3cm in liver segment 4. Dynamic CT showed early rim enhancement and gradual central enhancement. Contrast-enhanced CT also showed occlusion of the portal vein in segment 4. As the possibility of intrahepatic cholangiocarcinoma could not be excluded on imaging studies, we performed laparoscopic left medial sectionectomy. Histologically, the lesion showed diminished numbers of hepatocytes with increased collagen fibers compared with normal, with no patent portal vein. We considered this lesion a reactive lesion caused by collapse of the liver parenchyma owing to localized obstruction and loss of the portal vein. This lesion was pathologically diagnosed as portal biliopathy. We experienced an extremely rare case of intrahepatic mass-forming portal biliopathy that mimicked a hepatic tumor, which was diagnosed by laparoscopic resection. Portal biliopathy rarely forms intrahepatic mass lesions and must be distinguished from a malignant hepatic tumor.

Fully automated hybrid approach on conventional MRI for triaging clinically significant liver fibrosis: A multi-center cohort study.

Establishing reliable noninvasive tools to precisely diagnose clinically significant liver fibrosis (SF, ≥F2) remains an unmet need. We aimed to build a combined radiomics-clinic (CoRC) model for triaging SF and explore the additive value of the CoRC model to transient elastography-based liver stiffness measurement (FibroScan, TE-LSM). This retrospective study recruited 595 patients with biopsy-proven liver fibrosis at two centers between January 2015 and December 2021. At Center 1, the patients before December 2018 were randomly split into training (276) and internal test (118) sets, the remaining were time-independent as a temporal test set (96). Another data set (105) from Center 2 was collected for external testing. Radiomics scores were built with selected features from Deep learning-based (ResUNet) automated whole liver segmentations on MRI (T2FS and delayed enhanced-T1WI). The CoRC model incorporated radiomics scores and relevant clinical variables with logistic regression, comparing routine approaches. Diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC). The additive value of the CoRC model to TE-LSM was investigated, considering necroinflammation. The CoRC model achieved AUCs of 0.79 (0.70, 0.86), 0.82 (0.73, 0.89), and 0.81 (0.72-0.91), outperformed FIB-4, APRI (all p < 0.05) in the internal, temporal, and external test sets and maintained the discriminatory power in G0-1 subgroups (AUCs range, 0.85-0.86; all p < 0.05). The AUCs of joint CoRC-LSM model were 0.86 (0.79-0.94), and 0.81 (0.72-0.90) in the internal and temporal sets (p = 0.01). The CoRC model was useful for triaging SF, and may add value to TE-LSM.

Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images.

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring substantial expertise for interpretation. This research introduces an innovative strategy that integrates two-dimensional (2D) and three-dimensional (3D) deep learning models within a federated learning (FL) framework for precise segmentation of liver and tumor regions in medical images. The study utilized 131 CT scans from the Liver Tumor Segmentation (LiTS) challenge and demonstrated the superior efficiency and accuracy of the proposed Hybrid-ResUNet model with a Dice score of 0.9433 and an AUC of 0.9965 compared to ResNet and EfficientNet models. This FL approach is beneficial for conducting large-scale clinical trials while safeguarding patient privacy across healthcare settings. It facilitates active engagement in problem-solving, data collection, model development, and refinement. The study also addresses data imbalances in the FL context, showing resilience and highlighting local models' robust performance. Future research will concentrate on refining federated learning algorithms and their incorporation into the continuous implementation and deployment (CI/CD) processes in AI system operations, emphasizing the dynamic involvement of clients. We recommend a collaborative human-AI endeavor to enhance feature extraction and knowledge transfer. These improvements are intended to boost equitable and efficient data collaboration across various sectors in practical scenarios, offering a crucial guide for forthcoming research in medical AI.