Uterine segmentation and volume measurement in uterine fibroid patients' MRI using fuzzy C-mean algorithm and morphological operations

Artificial intelligence for instance segmentation of MRI: advancing efficiency and safety in laparoscopic myomectomy of broad ligament fibroids

Background

Uterine broad ligament fibroids present unique surgical challenges due to their proximity to vital pelvic structures. This study aimed to evaluate artificial intelligence (AI)-guided MRI instance segmentation for optimizing laparoscopic myomectomy outcomes.

Methods

In this trial, 120 patients with MRI-confirmed broad ligament fibroids were allocated to either AI-assisted group (n=60) or conventional MRI group (n=60). A deep learning model was developed to segment fibroids, uterine walls, and uterine cavity from preoperative MRI.

Result

Compared to conventional MRI guidance, AI assistance significantly reduced operative time (118 [112.25-125.00] vs. 140 [115.75-160.75] minutes; p<0.001). The AI group also demonstrated lower intraoperative blood loss (50 [50-100] vs. 85 [50-100] ml; p=0.01) and faster postoperative recovery (first flatus within 24 hours: (15[25.00%] vs. 29[48.33%], p=0.01).

Conclusion

This multidisciplinary AI system enhances surgical precision through millimeter-level anatomical delineation, demonstrating transformative potential for complex gynecologic oncology procedures. Clinical adoption of this approach could reduce intraoperative blood loss and iatrogenic complications, thereby promoting postoperative recovery.

3D segmentation of uterine fibroids based on deep supervision and an attention gate

Introduction

The segmentation of uterine fibroids is very important for the treatment of patients. However, uterine fibroids are small and have low contrast with surrounding tissue, making this task very challenging. To solve these problems, this paper proposes a 3D DA- VNet automatic segmentation method based on deep supervision and attention gate.

Methods

This method can accurately segment uterine fibroids in MRI images by convolutional information. We used 3DVnet as the underlying network structure and added a deep monitoring mechanism in the hidden layer. We introduce attention gates during the upsampling process to enhance focus on areas of interest. The network structure is composed of VNet, deep supervision module and attention gate module. The dataset contained 245 cases of uterine fibroids and was divided into a training set, a validation set, and a test set in a ratio of 6:2:2. A total of 147 patients' T2-weighted magnetic resonance (T2WI) images were used for training, 49 for validation, and 49 patients' MR Images were used for algorithm testing.

Results

Experimental results show that the proposed method achieves satisfactory segmentation results. Dice similarity coefficient (DSC), intersection ratio (IOU), sensitivity, precision and Hausdorff distance (HD) were 0.878, 0.784, 0.879, 0.885 and 11.180 mm, respectively.

Discussion

This shows that our proposed method can improve the automatic segmentation accuracy of magnetic resonance image (MRI) data of uterine fibroids to a certain extent.

nnU-Net based segmentation and 3D reconstruction of uterine fibroids with MRI images for HIFU surgery planning

High-Intensity Focused Ultrasound (HIFU) ablation represents a rapidly advancing non-invasive treatment modality that has achieved considerable success in addressing uterine fibroids, which constitute over 50% of benign gynecological tumors. Preoperative Magnetic Resonance Imaging (MRI) plays a pivotal role in the planning and guidance of HIFU surgery for uterine fibroids, wherein the segmentation of tumors holds critical significance. The segmentation process was previously manually executed by medical experts, entailing a time-consuming and labor-intensive procedure heavily reliant on clinical expertise. This study introduced deep learning-based nnU-Net models, offering a cost-effective approach for their application in the segmentation of uterine fibroids utilizing preoperative MRI images. Furthermore, 3D reconstruction of the segmented targets was implemented to guide HIFU surgery. The evaluation of segmentation and 3D reconstruction performance was conducted with a focus on enhancing the safety and effectiveness of HIFU surgery. Results demonstrated the nnU-Net's commendable performance in the segmentation of uterine fibroids and their surrounding organs. Specifically, 3D nnU-Net achieved Dice Similarity Coefficients (DSC) of 92.55% for the uterus, 95.63% for fibroids, 92.69% for the spine, 89.63% for the endometrium, 97.75% for the bladder, and 90.45% for the urethral orifice. Compared to other state-of-the-art methods such as HIFUNet, U-Net, R2U-Net, ConvUNeXt and 2D nnU-Net, 3D nnU-Net demonstrated significantly higher DSC values, highlighting its superior accuracy and robustness. In conclusion, the efficacy of the 3D nnU-Net model for automated segmentation of the uterus and its surrounding organs was robustly validated. When integrated with intra-operative ultrasound imaging, this segmentation method and 3D reconstruction hold substantial potential to enhance the safety and efficiency of HIFU surgery in the clinical treatment of uterine fibroids.

Diagnosis and management of uterine fibroids: current trends and future strategies

Uterine fibroids (UFs), leiomyomas or myomas, are a type of malignancy that affects the smooth muscle of the uterus, and it is most commonly detected in women of reproductive age. Uterine fibroids are benign monoclonal growths that emerge from uterine smooth muscle cells (myometrium) as well as fibroblasts. Uterine fibroid symptoms include abnormal menstrual bleeding leading to anaemia, tiredness, chronic vaginal discharge, and pain during periods. Other symptoms include protrusion of the abdomen, pain during intercourse, dysfunctions of bladder/bowel leading to urinary incontinence/retention, pain, and constipation. It is also associated with reproductive issues like impaired fertility, conceiving complications, and adverse obstetric outcomes. It is the leading cause of gynaecological hospitalisation in the American subcontinent and a common reason for the hysterectomy. Twenty-five percent of the reproductive women experience the symptoms of uterine fibroids, and among them, around 25% require hospitalization due to the severity of the disease. The frequency of the disease remains underestimated as many women stay asymptomatic and symptoms appear gradually; therefore, the condition remains undiagnosed. The exact frequency of uterine fibroids varies depending on the diagnosis, and the population investigated; nonetheless, the incidence of uterine fibroids in reproductive women ranges from 5.4 percent to 77 percent. The uterine fibroid treatment included painkillers, supplementation with iron, vitamin D3, birth control, hormone therapy, gonadotropin-releasing hormone (GnRH) agonists, drugs modulating the estrogen receptors, and surgical removal of the fibroids. However, more research needed at the level of gene to get a keen insight and treat the disease efficiently.

DARU‐Net: A dual attention residual U‐Net for uterine fibroids segmentation on MRI

PURPOSE

Uterine fibroid is the most common benign tumor in female reproductive organs. In order to guide the treatment, it is crucial to detect the location, shape, and size of the tumor. This study proposed a deep learning approach based on attention mechanisms to segment uterine fibroids automatically on preoperative Magnetic Resonance (MR) images.

METHODS

The proposed method is based on U-Net architecture and integrates two attention mechanisms: channel attention of squeeze-and-excitation (SE) blocks with residual connections, spatial attention of pyramid pooling module (PPM). We did the ablation study to verify the performance of these two attention mechanisms module and compared DARU-Net with other deep learning methods. All experiments were performed on a clinical dataset consisting of 150 cases collected from our hospital. Among them, 120 cases were used as the training set, and 30 cases are used as the test set. After preprocessing and data augmentation, we trained the network and tested it on the test dataset. We evaluated segmentation performance through the Dice similarity coefficient (DSC), precision, recall, and Jaccard index (JI).

RESULTS

The average DSC, precision, recall, and JI of DARU-Net reached 0.8066 ± 0.0956, 0.8233 ± 0.1255, 0.7913 ± 0.1304, and 0.6743 ± 0.1317. Compared with U-Net and other deep learning methods, DARU-Net was more accurate and stable.

CONCLUSION

This work proposed an optimized U-Net with channel and spatial attention mechanisms to segment uterine fibroids on preoperative MR images. Results showed that DARU-Net was able to accurately segment uterine fibroids from MR images.

HIFUNet: Multi-Class Segmentation of Uterine Regions From MR Images Using Global Convolutional Networks for HIFU Surgery Planning

Accurate segmentation of uterus, uterine fibroids, and spine from MR images is crucial for high intensity focused ultrasound (HIFU) therapy but remains still difficult to achieve because of 1) the large shape and size variations among individuals, 2) the low contrast between adjacent organs and tissues, and 3) the unknown number of uterine fibroids. To tackle this problem, in this paper, we propose a large kernel Encoder-Decoder Network based on a 2D segmentation model. The use of this large kernel can capture multi-scale contexts by enlarging the valid receptive field. In addition, a deep multiple atrous convolution block is also employed to enlarge the receptive field and extract denser feature maps. Our approach is compared to both conventional and other deep learning methods and the experimental results conducted on a large dataset show its effectiveness.

Real-time and multimodality image-guided intelligent HIFU therapy for uterine fibroid

Rationale: High-intensity focused ultrasound (HIFU) therapy represents a noninvasive surgical approach to treat uterine fibroids. The operation of HIFU therapy relies on the information provided by medical images. In current HIFU therapy, all operations such as positioning of the lesion in magnetic resonance (MR) and ultrasound (US) images are manually performed by specifically trained doctors. Manual processing is an important limitation of the efficiency of HIFU therapy. In this paper, we aim to provide an automatic and accurate image guidance system, intelligent diagnosis, and treatment strategy for HIFU therapy by combining multimodality information.

Methods: In intelligent HIFU therapy, medical information and treatment strategy are automatically processed and generated by a real-time image guidance system. The system comprises a novel multistage deep convolutional neural network for preoperative diagnosis and a nonrigid US lesion tracking procedure for HIFU intraoperative image-assisted treatment. In the process of intelligent therapy, the treatment area is determined from the autogenerated lesion area. Based on the autodetected treatment area, the HIFU foci are distributed automatically according to the treatment strategy. Moreover, an image-based unexpected movement warning and other physiological monitoring are used during the intelligent treatment procedure for safety assurance.

Results: In the experiment, we integrated the intelligent treatment system on a commercial HIFU treatment device, and eight clinical experiments were performed. In the clinical validation, eight randomly selected clinical cases were used to verify the feasibility of the system. The results of the quantitative experiment indicated that our intelligent system met the HIFU clinical tracking accuracy and speed requirements. Moreover, the results of simulated repeated experiments confirmed that the autodistributed HIFU focus reached the level of intermediate clinical doctors. Operations performed by junior- or middle-level operators with the assistance of the proposed system can reach the level of operation performed by senior doctors. Various experiments prove that our proposed intelligent HIFU therapy process is feasible for treating common uterine fibroid cases.

Conclusion: We propose an intelligent HIFU therapy for uterine fibroid which integrates multiple medical information processing procedures. The experiment results demonstrated that the proposed procedures and methods can achieve monitored and automatic HIFU diagnosis and treatment. This research provides a possibility for intelligent and automatic noninvasive therapy for uterine fibroid.

Automatic segmentation of the uterus on MRI using a convolutional neural network

BACKGROUND

This study was performed to evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI by using images of major uterine disorders.

METHODS

This study included 122 female patients (14 with uterine endometrial cancer, 15 with uterine cervical cancer, and 55 with uterine leiomyoma). U-net architecture optimized for our research was used for automatic segmentation. Three-fold cross-validation was performed for validation. The results of manual segmentation of the uterus by a radiologist on T2-weighted sagittal images were used as the gold standard. Dice similarity coefficient (DSC) and mean absolute distance (MAD) were used for quantitative evaluation of the automatic segmentation. Visual evaluation using a 4-point scale was performed by two radiologists. DSC, MAD, and the score of the visual evaluation were compared between uteruses with and without uterine disorders.

RESULTS

The mean DSC of our model for all patients was 0.82. The mean DSCs for patients with and without uterine disorders were 0.84 and 0.78, respectively (p = 0.19). The mean MADs for patients with and without uterine disorders were 18.5 and 21.4 [pixels], respectively (p = 0.39). The scores of the visual evaluation were not significantly different between uteruses with and without uterine disorders.

CONCLUSIONS

Fully automatic uterine segmentation with our modified U-net was clinically feasible. The performance of the segmentation of our model was not influenced by the presence of uterine disorders.

Evaluation of pre-surgical models for uterine surgery by use of three-dimensional printing and mold casting

We propose an approach to supporting pre-surgical planning for the uterus by integrating medical image analysis and physical model generation based on 3D printing. With our method, we first segment the patient-specific anatomy and lesions of the uterus on MR images; then, we create a 3D physical model, an exact replica of the patient's uterus in terms of size and softness, with transparency for easy observation of the internal structures of the uterus. In our experiments, we created pre-surgical models of hysterectomy for five patients who had been diagnosed to have uterine endometrial cancer. An experienced radiologist, the surgeons, and all of the patients cooperated in our experiment for carrying out subjective evaluations of the usefulness of our model. The accuracy of the physical models was evaluated quantitatively by comparison between the MR images of the patients and the CT images of the models. The results showed that the mean values of the errors in gap ranged from 1.19 to 2.22 mm, which was satisfactory for the surgeons. The feedback from both surgeons and patients demonstrated the usefulness and convenience of the models for efficient patient explanation understanding and pre-surgical planning by surgeons.

Colorectal Cancer Staging Using Three Clustering Methods Based on Preoperative Clinical Findings

Determination of the colorectal cancer stage is possible only after surgery based on pathology results. However, sometimes this may prove impossible. The aim of the present study was to determine colorectal cancer stage using three clustering methods based on preoperative clinical findings. All patients referred to the Colorectal Research Center of Shiraz University of Medical Sciences for colorectal cancer surgery during 2006 to 2014 were enrolled in the study. Accordingly, 117 cases participated. Three clustering algorithms were utilized including k-means, hierarchical and fuzzy c-means clustering methods. External validity measures such as sensitivity, specificity and accuracy were used for evaluation of the methods. The results revealed maximum accuracy and sensitivity values for the hierarchical and a maximum specificity value for the fuzzy c-means clustering methods. Furthermore, according to the internal validity measures for the present data set, the optimal number of clusters was two (silhouette coefficient) and the fuzzy c-means algorithm was more appropriate than the k-means clustering approach by increasing the number of clusters.

Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments

Uterine fibroids are benign tumors that can affect female patients during reproductive years. Magnetic resonance-guided focused ultrasound (MRgFUS) represents a noninvasive approach that uses thermal ablation principles to treat symptomatic fibroids. During traditional treatment planning, uterus, fibroids, and surrounding organs at risk must be manually marked on MR images by an operator. After treatment, an operator must segment, again manually, treated areas to evaluate the non-perfused volume (NPV) inside the fibroids. Both pre- and post-treatment procedures are time-consuming and operator-dependent. This paper presents a novel method, based on an advanced direct region detection model, for fibroid segmentation in MR images to address MRgFUS post-treatment segmentation issues. An incremental procedure is proposed: split-and-merge algorithm results are employed as multiple seed-region selections by an adaptive region growing procedure. The proposed approach segments multiple fibroids with different pixel intensity, even in the same MR image. The method was evaluated using area-based and distance-based metrics and was compared with other similar works in the literature. Segmentation results, performed on 14 patients, demonstrated the effectiveness of the proposed approach showing a sensitivity of 84.05 %, a specificity of 92.84 %, and a speedup factor of 1.56× with respect to classic region growing implementations (average values).

Uterine artery embolization for treatment of symptomatic fibroids; a single institution experience

BACKGROUND

Uterine fibroids are the most common reproductive tract tumours in females. Uterine artery embolization (UAE) is a fertility-sparing procedure for treatment of symptomatic fibroids. We evaluated the efficacy and safety of UAE in the treatment of 118 patients with symptomatic uterine fibroids in a single Academic Centre in the West of Ireland to determine whether fibroid and uterine size affect clinical outcomes and complications.

METHODS

This was a retrospective cohort of 118 patients who underwent UAE for treatment of symptomatic fibroids between November 2006 and August 2011. Diagnosis of fibroids in symptomatic patients was established by magnetic resonance imaging (MRI) and/or transabdominal ultrasonography (US). Three different embolic agents were used. All patients had at least one follow-up using MRI, at three and/or 12 months. A non-validated questionnaire was used to report patient satisfaction with regards to symptoms improvement on a yes-or-no basis.

RESULTS

Mean fibroid volume, uterine size and dominant fibroid size were significantly reduced at three months and one year follow-up (p = 0.00) and that was tallied with symptoms improvement (p < 0.05). Overall patient satisfaction at three months was 84% falling to 75.9% by 12 months (all p < 0.05). Few complications were reported (2.5%). No significant difference was observed in safety or efficacy for different embolic agents.

CONCLUSION

The study confirms the safety and efficacy of UAE in the treatment of symptomatic fibroids. Hippokratia 2014; 18 (3): 258-261.

How to start interventional radiology

Interventional techniques aim to find safer and better ways to treat vascular diseases even in many instances, the interventional radiology solutions has been considered the only treatment option for the patients. Interventional radiologists are specialists who perform minimally invasive procedures instead of surgery or other treatments. These procedures apply various imaging and catheterization procedures in order to diagnose and treat diseases. In each country, interventional radiology practice establishment of varies according to local factors, but following a standard strategy seems better to set up this facility. According to above mentioned points, we decided to establish this specialty in our hospital since 2001 as the pioneer center in Iran. In this presentation we will discuss about our experience for start interventional radiology.

Uterine artery embolization for treatment of symptomatic fibroids: a review of the evidence

Fibroids are the most common benign tumors of the uterus during female reproductive age. Uterine artery embolization (UAE) using embolic particles (PVA, Gelfoam) to occlude the uterine arteries, have been reported as a relatively safe, effective, and durable nonsurgical alternative to hysterectomy in diminishing fibroid-related symptoms. To block the arterial blood supply to the fibroid completely, UAE is typically performed in both uterine arteries by an experienced interventional radiologist. Reduction in menorrhagia has been reported as 80-93 percent and the mean decrease in fibroid size varies from 50-78% in the literature. In our center improvement in menstrual bleeding after 6 months was 80.3%, and uterine fibroids underwent shrinkage of 63.7±33.7% after12 months. Complication rate including amenorrhea ranges from 1% - 7% in the literature. UAE may be followed by menopause in 1% of cases. Nevertheless, it is usually encountered in women in their late 40s. It seems that the future of UAE depends on optimal selection of patients according to volume-shrinkage prediction and fertility outcome. Although pregnancy is possible after embolization, however neither fertility preservation nor improvement can be guaranteed following UAE. Indeed, Women who desire to become pregnant should be cautioned about potential complications during pregnancy. The aim of this review is to discuss about the efficacy, safety, technique, and choice of embolic agent. Also we present the effects of this technique on fertility and pregnancy outcome and also methods for dose reduction during this procedure.

Comparing ovarian radiation doses in flat-panel and conventional angiography during uterine artery embolization: a randomized clinical trial

Background

Uterine artery embolization (UAE) is a minimally invasive procedure performed under fluoroscopy for the treatment of uterine fibroids and accompanied by radiation exposure.

Objectives

To compare ovarian radiation doses during uterine artery embolization (UAE) in patients using conventional digital subtraction angiography (DSA) with those using digital flat-panel technology.

Patients and Methods

Thirty women who were candidates for UAE were randomly enrolled for one of the two angiographic systems. Ovarian doses were calculated according to in-vitro phantom study results using entrance and exit doses and were compared between the two groups.

Results

The mean right entrance dose was 1586±1221 mGy in the conventional and 522.3±400.1 mGy in the flat panel group (P=0.005). These figures were 1470±1170 mGy and 456±396 mGy, respectively for the left side (P=0.006). The mean right exit dose was 18.8±12.3 for the conventional and 9.4±6.4 mGy for the flat panel group (P=0.013). These figures were 16.7±11.3 and 10.2±7.2 mGy, respectively for the left side (P=0.06). The mean right ovarian dose was 139.9±92 in the conventional and 23.6±16.2 mGy in the flat panel group (P<0.0001). These figures were 101.7±77.6 and 24.6±16.9 mGy, respectively for the left side (P=0.002).

Conclusion

Flat panel system can significantly reduce the ovarian radiation dose during UAE compared with conventional DSA.