Disseminated Histoplasmosis presenting with panniculitis and macrophage activation syndrome in a patient undergoing immunosuppressive therapy for Mixed Connective Tissue Disease (MCTD).

Histoplasmosis, a fungal infection caused by Histoplasma capsulatum, is endemic in many parts of the world. However, this is not common in Japan. We herein present a unique case of military histoplasmosis in a 45-year-old female with mixed connective tissue disease (MCTD) who was receiving immunosuppressive therapy. The histological findings coupled with molecular confirmation led to final a diagnosis. This case emphasizes the diagnostic challenges associated with histoplasmosis in immunocompromised patients and underscores the importance of considering it in the differential diagnosis of any atypical presentation in rheumatic patients.

Federated 3D multi-organ segmentation with partially labeled and unlabeled data.

PURPOSE: This paper considers a new problem setting for multi-organ segmentation based on the following observations. In reality, (1) collecting a large-scale dataset from various institutes is usually impeded due to privacy issues; (2) many images are not labeled since the slice-by-slice annotation is costly; and (3) datasets may exhibit inconsistent, partial annotations across different institutes. Learning a federated model from these distributed, partially labeled, and unlabeled samples is an unexplored problem. METHODS: To simulate this multi-organ segmentation problem, several distributed clients and a central server are maintained. The central server coordinates with clients to learn a global model using distributed private datasets, which comprise a small part of partially labeled images and a large part of unlabeled images. To address this problem, a practical framework that unifies partially supervised learning (PSL), semi-supervised learning (SSL), and federated learning (FL) paradigms with PSL, SSL, and FL modules is proposed. The PSL module manages to learn from partially labeled samples. The SSL module extracts valuable information from unlabeled data. Besides, the FL module aggregates local information from distributed clients to generate a global statistical model. With the collaboration of three modules, the presented scheme could take advantage of these distributed imperfect datasets to train a generalizable model. RESULTS: The proposed method was extensively evaluated with multiple abdominal CT datasets, achieving an average result of 84.83% in Dice and 41.62 mm in 95HD for multi-organ (liver, spleen, and stomach) segmentation. Moreover, its efficacy in transfer learning further demonstrated its good generalization ability for downstream segmentation tasks. CONCLUSION: This study considers a novel problem of multi-organ segmentation, which aims to develop a generalizable model using distributed, partially labeled, and unlabeled CT images. A practical framework is presented, which, through extensive validation, has proved to be an effective solution, demonstrating strong potential in addressing this challenging problem.

A giant peripheral ossifying fibroma of the maxilla with extreme difficulty in clinical differentiation from malignancy: a case report and review of the literature.

BACKGROUND: Peripheral ossifying fibroma is a nonneoplastic inflammatory hyperplasia that originates in the periodontal ligament or periosteum in response to chronic mechanical irritation. Peripheral ossifying fibroma develops more commonly in young females as a solitary, slow-growing, exophytic nodular mass of the gingiva, no more than 2 cm in diameter. While various synonyms have been used to refer to peripheral ossifying fibroma, very similar names have also been applied to neoplastic diseases that are pathologically distinct from peripheral ossifying fibroma, causing considerable nomenclatural confusion. Herein, we report our experience with an unusual giant peripheral ossifying fibroma with a differential diagnostic challenge in distinguishing it from a malignancy. CASE PRESENTATION: A 68-year-old Japanese male was referred to our department with a suspected gingival malignancy presenting with an elastic hard, pedunculated, exophytic mass 60 mm in diameter in the right maxillary gingiva. In addition to computed tomography showing extensive bone destruction in the right maxillary alveolus, positron emission tomography with computed tomography revealed fluorodeoxyglucose hyperaccumulation in the gingival lesion. Although these clinical findings were highly suggestive of malignancy, repeated preoperative biopsies showed no evidence of malignancy. Since even intraoperative frozen histological examination revealed no malignancy, surgical resection was performed in the form of partial maxillectomy for benign disease, followed by thorough curettage of the surrounding granulation tissue and alveolar bone. Histologically, the excised mass consisted primarily of a fibrous component with sparse proliferation of atypical fibroblast-like cells, partly comprising ossification, leading to a final diagnosis of peripheral ossifying fibroma. No relapse was observed at the 10-month follow-up. CONCLUSIONS: The clinical presentation of giant peripheral ossifying fibromas can make the differential diagnosis from malignancy difficult. Proper diagnosis relies on recognition of the characteristic histopathology and identification of the underlying chronic mechanical stimuli, while successful treatment mandates complete excision of the lesion and optimization of oral hygiene. Complicated terminological issues associated with peripheral ossifying fibroma require appropriate interpretation and sufficient awareness of the disease names to avoid diagnostic confusion and provide optimal management.

Towards better laparoscopic video segmentation: A class-wise contrastive learning approach with multi-scale feature extraction.

The task of segmentation is integral to computer-aided surgery systems. Given the privacy concerns associated with medical data, collecting a large amount of annotated data for training is challenging. Unsupervised learning techniques, such as contrastive learning, have shown powerful capabilities in learning image-level representations from unlabelled data. This study leverages classification labels to enhance the accuracy of the segmentation model trained on limited annotated data. The method uses a multi-scale projection head to extract image features at various scales. The partitioning method for positive sample pairs is then improved to perform contrastive learning on the extracted features at each scale to effectively represent the differences between positive and negative samples in contrastive learning. Furthermore, the model is trained simultaneously with both segmentation labels and classification labels. This enables the model to extract features more effectively from each segmentation target class and further accelerates the convergence speed. The method was validated using the publicly available CholecSeg8k dataset for comprehensive abdominal cavity surgical segmentation. Compared to select existing methods, the proposed approach significantly enhances segmentation performance, even with a small labelled subset (1-10%) of the dataset, showcasing a superior intersection over union (IoU) score.

Revisiting instrument segmentation: Learning from decentralized surgical sequences with various imperfect annotations.

This paper focuses on a new and challenging problem related to instrument segmentation. This paper aims to learn a generalizable model from distributed datasets with various imperfect annotations. Collecting a large-scale dataset for centralized learning is usually impeded due to data silos and privacy issues. Besides, local clients, such as hospitals or medical institutes, may hold datasets with diverse and imperfect annotations. These datasets can include scarce annotations (many samples are unlabelled), noisy labels prone to errors, and scribble annotations with less precision. Federated learning (FL) has emerged as an attractive paradigm for developing global models with these locally distributed datasets. However, its potential in instrument segmentation has yet to be fully investigated. Moreover, the problem of learning from various imperfect annotations in an FL setup is rarely studied, even though it presents a more practical and beneficial scenario. This work rethinks instrument segmentation in such a setting and propose a practical FL framework for this issue. Notably, this approach surpassed centralized learning under various imperfect annotation settings. This method established a foundational benchmark, and future work can build upon it by considering each client owning various annotations and aligning closer with real-world complexities.

YOLOv7-RepFPN: Improving real-time performance of laparoscopic tool detection on embedded systems.

This study focuses on enhancing the inference speed of laparoscopic tool detection on embedded devices. Laparoscopy, a minimally invasive surgery technique, markedly reduces patient recovery times and postoperative complications. Real-time laparoscopic tool detection helps assisting laparoscopy by providing information for surgical navigation, and its implementation on embedded devices is gaining interest due to the portability, network independence and scalability of the devices. However, embedded devices often face computation resource limitations, potentially hindering inference speed. To mitigate this concern, the work introduces a two-fold modification to the YOLOv7 model: the feature channels and integrate RepBlock is halved, yielding the YOLOv7-RepFPN model. This configuration leads to a significant reduction in computational complexity. Additionally, the focal EIoU (efficient intersection of union) loss function is employed for bounding box regression. Experimental results on an embedded device demonstrate that for frame-by-frame laparoscopic tool detection, the proposed YOLOv7-RepFPN achieved an mAP of 88.2% (with IoU set to 0.5) on a custom dataset based on EndoVis17, and an inference speed of 62.9 FPS. Contrasting with the original YOLOv7, which garnered an 89.3% mAP and 41.8 FPS under identical conditions, the methodology enhances the speed by 21.1 FPS while maintaining detection accuracy. This emphasizes the effectiveness of the work.

Spontaneous rupture of a uterine leiomyoma accompanied by a hematoma appearing as a cystic lesion on imaging: A case report.

The rupture of a uterine leiomyoma is a rare complication. We report a case of ruptured leiomyoma that formed a hematoma that was initially suggestive of an ovarian origin. Magnetic resonance imaging revealed intact ovaries and a cystic lesion adjacent to leiomyomas. During surgery, the cystic lesion was found to be a hematoma caused by a rupture of the leiomyoma.

A clinical assessment of three-dimensional-printed liver model navigation for thrice or more repeated hepatectomy based on a conversation analysis.

PURPOSES: We performed a conversation analysis of the speech conducted among the surgical team during three-dimensional (3D)-printed liver model navigation for thrice or more repeated hepatectomy (TMRH). METHODS: Seventeen patients underwent 3D-printed liver navigation surgery for TMRH. After transcription of the utterances recorded during surgery, the transcribed utterances were coded by the utterer, utterance object, utterance content, sensor, and surgical process during conversation. We then analyzed the utterances and clarified the association between the surgical process and conversation through the intraoperative reference of the 3D-printed liver. RESULTS: In total, 130 conversations including 1648 segments were recorded. Utterance coding showed that the operator/assistant, 3D-printed liver/real liver, fact check (F)/plan check (Pc), visual check/tactile check, and confirmation of planned resection or preservation target (T)/confirmation of planned or ongoing resection line (L) accounted for 791/857, 885/763, 1148/500, 1208/440, and 1304/344 segments, respectively. The utterance's proportions of assistants, F, F of T on 3D-printed liver, F of T on real liver, and Pc of L on 3D-printed liver were significantly higher during non-expert surgeries than during expert surgeries. Confirming the surgical process with both 3D-printed liver and real liver and performing planning using a 3D-printed liver facilitates the safe implementation of TMRH, regardless of the surgeon's experience. CONCLUSIONS: The present study, using a unique conversation analysis, provided the first evidence for the clinical value of 3D-printed liver for TMRH for anatomical guidance of non-expert surgeons.

Anatomical attention can help to segment the dilated pancreatic duct in abdominal CT.

PURPOSE: Pancreatic duct dilation is associated with an increased risk of pancreatic cancer, the most lethal malignancy with the lowest 5-year relative survival rate. Automatic segmentation of the dilated pancreatic duct from contrast-enhanced CT scans would facilitate early diagnosis. However, pancreatic duct segmentation poses challenges due to its small anatomical structure and poor contrast in abdominal CT. In this work, we investigate an anatomical attention strategy to address this issue. METHODS: Our proposed anatomical attention strategy consists of two steps: pancreas localization and pancreatic duct segmentation. The coarse pancreatic mask segmentation is used to guide the fully convolutional networks (FCNs) to concentrate on the pancreas' anatomy and disregard unnecessary features. We further apply a multi-scale aggregation scheme to leverage the information from different scales. Moreover, we integrate the tubular structure enhancement as an additional input channel of FCN. RESULTS: We performed extensive experiments on 30 cases of contrast-enhanced abdominal CT volumes. To evaluate the pancreatic duct segmentation performance, we employed four measurements, including the Dice similarity coefficient (DSC), sensitivity, normalized surface distance, and 95 percentile Hausdorff distance. The average DSC achieves 55.7%, surpassing other pancreatic duct segmentation methods on single-phase CT scans only. CONCLUSIONS: We proposed an anatomical attention-based strategy for the dilated pancreatic duct segmentation. Our proposed strategy significantly outperforms earlier approaches. The attention mechanism helps to focus on the pancreas region, while the enhancement of the tubular structure enables FCNs to capture the vessel-like structure. The proposed technique might be applied to other tube-like structure segmentation tasks within targeted anatomies.

Development of real-time navigation system for laparoscopic hepatectomy using magnetic micro sensor.

BACKGROUND: We report a new real-time navigation system for laparoscopic hepatectomy (LH), which resembles a car navigation system. MATERIAL AND METHODS: Virtual three-dimensional liver and body images were reconstructed using the "New-VES" system, which worked as roadmap during surgery. Several points of the patient's body were registered in virtual images using a magnetic position sensor (MPS). A magnetic transmitter, corresponding to an artificial satellite, was placed about 40 cm above the patient's body. Another MPS, corresponding to a GPS antenna, was fixed on the handling part of the laparoscope. Fiducial registration error (FRE, an error between real and virtual lengths) was utilized to evaluate the accuracy of this system. RESULTS: Twenty-one patients underwent LH with this system. Mean FRE of the initial five patients was 17.7 mm. Mean FRE of eight patients in whom MDCT was taken using radiological markers for registration of body parts as first improvement, was reduced to 10.2 mm (p = .014). As second improvement, a new MPS as an intraoperative body position sensor was fixed on the right-sided chest wall for automatic correction of postural gap. The preoperative and postoperative mean FREs of 8 patients with both improvements were 11.1 mm and 10.1 mm (p = .250). CONCLUSIONS: Our system may provide a promising option that virtually guides LH.

IgG4-related pachyleptomeningitis with inflammatory pseudotumor.

•We report the first case of IgG4-related pachyleptomeningitis.•Our case showed also an inflammatory pseudotumor on the side ipsilateral to the pachyleptomeningitis.•The pachyleptomeningitis is probably due to inflammation from the dural pseudotumor spreading along the adjacent meninges.

Artificial intelligence in a prediction model for postendoscopic retrograde cholangiopancreatography pancreatitis.

OBJECTIVES: In this study we aimed to develop an artificial intelligence-based model for predicting postendoscopic retrograde cholangiopancreatography (ERCP) pancreatitis (PEP). METHODS: We retrospectively reviewed ERCP patients at Nagoya University Hospital (NUH) and Toyota Memorial Hospital (TMH). We constructed two prediction models, a random forest (RF), one of the machine-learning algorithms, and a logistic regression (LR) model. First, we selected features of each model from 40 possible features. Then the models were trained and validated using three fold cross-validation in the NUH cohort and tested in the TMH cohort. The area under the receiver operating characteristic curve (AUROC) was used to assess model performance. Finally, using the output parameters of the RF model, we classified the patients into low-, medium-, and high-risk groups. RESULTS: A total of 615 patients at NUH and 544 patients at TMH were enrolled. Ten features were selected for the RF model, including albumin, creatinine, biliary tract cancer, pancreatic cancer, bile duct stone, total procedure time, pancreatic duct injection, pancreatic guidewire-assisted technique without a pancreatic stent, intraductal ultrasonography, and bile duct biopsy. In the three fold cross-validation, the RF model showed better predictive ability than the LR model (AUROC 0.821 vs. 0.660). In the test, the RF model also showed better performance (AUROC 0.770 vs. 0.663, P = 0.002). Based on the RF model, we classified the patients according to the incidence of PEP (2.9%, 10.0%, and 23.9%). CONCLUSION: We developed an RF model. Machine-learning algorithms could be powerful tools to develop accurate prediction models.

SGSR: style-subnets-assisted generative latent bank for large-factor super-resolution with registered medical image dataset.

PURPOSE: We propose a large-factor super-resolution (SR) method for performing SR on registered medical image datasets. Conventional SR approaches use low-resolution (LR) and high-resolution (HR) image pairs to train a deep convolutional neural network (DCN). However, LR-HR images in medical imaging are commonly acquired from different imaging devices, and acquiring LR-HR image pairs needs registration. Registered LR-HR images have registration errors inevitably. Using LR-HR images with registration error for training an SR DCN causes collapsed SR results. To address these challenges, we introduce a novel SR approach designed specifically for registered LR-HR medical images. METHODS: We propose style-subnets-assisted generative latent bank for large-factor super-resolution (SGSR) trained with registered medical image datasets. Pre-trained generative models named generative latent bank (GLB), which stores rich image priors, can be applied in SR to generate realistic and faithful images. We improve GLB by newly introducing style-subnets-assisted GLB (S-GLB). We also propose a novel inter-uncertainty loss to boost our method's performance. Introducing more spatial information by inputting adjacent slices further improved the results. RESULTS: SGSR outperforms state-of-the-art (SOTA) supervised SR methods qualitatively and quantitatively on multiple datasets. SGSR achieved higher reconstruction accuracy than recently supervised baselines by increasing peak signal-to-noise ratio from 32.628 to 34.206 dB. CONCLUSION: SGSR performs large-factor SR while given a registered LR-HR medical image dataset with registration error for training. SGSR's results have both realistic textures and accurate anatomical structures due to favorable quantitative and qualitative results. Experiments on multiple datasets demonstrated SGSR's superiority over other SOTA methods. SR medical images generated by SGSR are expected to improve the accuracy of pre-surgery diagnosis and reduce patient burden.

Endoscope Automation Framework with Hierarchical Control and Interactive Perception for Multi-Tool Tracking in Minimally Invasive Surgery.

In the context of Minimally Invasive Surgery, surgeons mainly rely on visual feedback during medical operations. In common procedures such as tissue resection, the automation of endoscopic control is crucial yet challenging, particularly due to the interactive dynamics of multi-agent operations and the necessity for real-time adaptation. This paper introduces a novel framework that unites a Hierarchical Quadratic Programming controller with an advanced interactive perception module. This integration addresses the need for adaptive visual field control and robust tool tracking in the operating scene, ensuring that surgeons and assistants have optimal viewpoint throughout the surgical task. The proposed framework handles multiple objectives within predefined thresholds, ensuring efficient tracking even amidst changes in operating backgrounds, varying lighting conditions, and partial occlusions. Empirical validations in scenarios involving single, double, and quadruple tool tracking during tissue resection tasks have underscored the system's robustness and adaptability. The positive feedback from user studies, coupled with the low cognitive and physical strain reported by surgeons and assistants, highlight the system's potential for real-world application.

Transcriptome of Oral Cancer Cells Adapted to Suspension Culture Is Potentially Related to Cancer Progressive Phenotypes.

BACKGROUND/AIM: Cervical lymph node metastasis worsens oral cancer prognosis. Cancer cells with high metastatic ability can delay or resist apoptosis and survive in the floating condition during circulation. The involved genes and pathways in this process remain largely unknown. This study aimed to establish an oral cancer cell line adapted to suspension culture by in vitro selection and perform gene expression analysis. MATERIALS AND METHODS: The oral cancer cell subline adapted to suspension culture was isolated by in vitro selection from the oral cancer cell line, HSC-3. The transcriptome profiles of HSC-3 and its subline were compared using gene expression microarrays. Gene Ontology (GO) enrichment analysis, Gene Set Enrichment Analysis (GSEA), and Ingenuity Pathway Analysis (IPA) were performed to predict the involved pathways and molecules in cancer progression. RESULTS: The subline was designated as HSC-3S5 The cellular viability of HSC-3S5 cells at the suspension culture was higher than that of HSC-3 cells. A total of 961 genes were differentially expressed between HSC-3 and HSC-3S5 cells under the threshold cut-off (FDR-adjusted p-value of <0.05 and absolute fold change of >1.5). GO terms, such as growth regulation, were enriched in the DEGs. GSEA revealed the association between the DEGs and significant gene sets, including metastasis and stemness. IPA predicted that the proliferation-related pathways were enhanced while the apoptotic pathway was inhibited in HSC-3S5 cells compared to HSC-3 cells. CONCLUSION: Our transcriptome analysis revealed several potentially activated pathways and molecules in the floating-adapted oral cancer cells and indicated molecular implications for cancer progression.

Artificial intelligence for evaluating the risk of gastric cancer: reliable detection and scoring of intestinal metaplasia with deep learning algorithms.

BACKGROUND AND AIMS: Gastric cancer (GC) is associated with chronic gastritis. To evaluate the risk, the Operative Link on Gastric Intestinal Metaplasia Assessment (OLGIM) system was constructed and showed a higher GC risk in stage III or IV patients, determined by the degree of intestinal metaplasia (IM). Although the OLGIM system is useful, evaluating the degree of IM requires substantial experience to produce precise scoring. Whole-slide imaging is becoming routine, but most artificial intelligence (AI) systems in pathology are focused on neoplastic lesions. METHODS: Hematoxylin and eosin-stained slides were scanned. Images were divided into each gastric biopsy tissue sample and labeled with an IM score. IM was scored as follows: 0 (no IM), 1 (mild IM), 2 (moderate IM), and 3 (severe IM). Overall, 5753 images were prepared. A deep convolutional neural network (DCNN) model, ResNet50, was used for classification. RESULTS: ResNet50 classified images with and without IM with a sensitivity of 97.7% and specificity of 94.6%. IM scores 2 and 3, involved as criteria of stage III or IV in the OLGIM system, were classified by ResNet50 in 18%. The respective sensitivity and specificity values of classifying IM between scores 0 and 1 and 2 and 3 were 98.5% and 94.9%, respectively. The IM scores classified by pathologists and the AI system were different in only 438 images (7.6%), and we found that ResNet50 tended to miss small foci of IM but successfully identified minimal IM areas that pathologists missed during the review. CONCLUSIONS: Our findings suggested that this AI system would contribute to evaluating the risk of GC accuracy, reliability, and repeatability with worldwide standardization.

Efficacy of concurrent chemoradiotherapy with retrograde super selective intra-arterial infusion combined with cetuximab for synchronous multifocal oral squamous cell carcinomas.

BACKGROUND: The incidence of multicentric oral cancer is increasing. However, treatment encounters difficulty if each tumor needs to be treated simultaneously. The objective of this clinical case report is to highlight the effect of concurrent chemoradiotherapy with retrograde superselective intra-arterial infusion combined with systemic administration of cetuximab on synchronous multifocal oral squamous cell carcinomas. CASE PRESENTATION: A 70-year-old man presented to the hospital with multiple tumors and oral pain. Three independent tumors were found in the right dorsal tongue, left edge of the tongue, and left lower lip. Based on the characteristic appearance of the lesions and further evaluation, clinical diagnoses of right tongue cancer "T3", left tongue cancer "T2" and lower left lip cancer "T1", N2cM0 were made. Treatment was initiated with systemic administration of cetuximab, followed by intra-arterial chemoradiotherapy. Treatment results were complete response on all three local lesions, and left neck dissection was performed following the initial treatment. The patient showed no evidence of recurrence during the 4 years follow-up period. CONCLUSIONS: This novel combination treatment seems to be a promising strategy for patients with synchronous multifocal oral squamous cell carcinoma.

The temporal and contextual stability of activity levels in hippocampal CA1 cells.

Recent long-term optical imaging studies have demonstrated that the activity levels of hippocampal neurons in a familiar environment change on a daily to weekly basis. However, it is unclear whether there is any time-invariant property in the cells' neural representations. In this study, using miniature fluorescence microscopy, we measured the neural activity of the mouse hippocampus in four different environments every 3 d. Although the activity level of hippocampal neurons fluctuated greatly in each environment across days, we found a significant correlation between the activity levels for different days, and the correlation was higher for averaged activity levels across multiple environments. When the number of environments used for averaging was increased, a higher activity correlation was observed. Furthermore, the number of environments in which a cell showed activity was preserved. Cells that showed place cell activity in many environments had greater spatial information content and more stable spatial representation, and thus carried more abundant and stable information about the current position. In contrast, cells that were active only in a small number of environments provided sparse representation for the environment. These results suggest that each cell has not only an inherent activity level but also play a characteristic role in the coding of space.