Initial Contour Research Articles

Analysis Hybrid Approach for Multi-Objective Optimization-based segmentation of fuzzy contours in mammogram using artificial plant optimization algorithm

It is observed that importance is given to the identification of tumor in case of medical image diagnosis. It has been found that in women breast cancer is increasing and due to which it has an impact in the death rate also. In medical domain radiologist generally uses the computer aided detection methods (CAD ). Medical image often contains the noise and some fuzziness in the picture border so the identification of the tumor is having problem and it becomes challenging task. by Owing to the noise and fuzziness of the picture border, identifying tumors on mammograms is challenging. Even though Active contour is an effective method for the segmentation but It is having problem related to local minima. The proposed method is a hybrid which is consist of multi objective optimization and Gravitational search algorithm (GSA) used for segmentation after that results are given to an Artificial plant optimization algorithm (APOA). The operations involve in this process includes some subprocesses primarily contour initialization is done for ROI followed by segmentation through GSA and multi-objective optimization and then APOA is applied. To evaluate the results two different databases are used mini-MIAS and DDSM databases. Results are also compared with other research work carried out earlier. It is observed that our proposed method gives better output then other.

Thermal Imaging-Based Abnormal Heating Detection for High-Voltage Power Equipment

Thermal infrared imaging could detect hidden faults in various types of high-voltage power equipment, which is of great significance for power inspections. However, there are still certain issues with thermal-imaging-based abnormal heating detection methods due to varying appearances of abnormal regions and complex temperature interference from backgrounds. To solve these problems, a contour-based instance segmentation network is first proposed to utilize thermal (T) and visual (RGB) images, realizing high-accuracy segmentation against complex and changing environments. Specifically, modality-specific features are encoded via two-stream backbones and fused in spatial, channel, and frequency domains. In this way, modality differences are well handled, and effective complementary information is extracted for object detection and contour initialization. The transformer decoder is further utilized to explore the long-range relationships between contour points with background points, and to achieve the deformation of contour points. Then, the auto-encoder-based reconstruction network is developed to learn the distribution of power equipment using the proposed random argument strategy. Meanwhile, the UNet-like discriminative network directly explores the differences between the reconstructed and original image, capturing the deviation of poor reconstruction regions for abnormal heating detection. Many images are acquired in transformer substations with different weathers and day times to build the datasets with pixel-level annotation. Several extensive experiments are conducted for qualitative and quantitative evaluation, while the comparison results fully prove the effectiveness and robustness of the proposed instance segmentation method. The practicality and performance of the proposed abnormal heating detection method are evaluated on image patches with different kinds of insulators.

Fractional differentiation-based variational level set model for noisy image segmentation without contour initialization

Abstract Noisy image segmentation is a hot topic in image analysis. In this paper, we present a novel methodology for tackling this issue through the integration of fractional differentiation in the frequency domain with a variational level set model (VLSM), which eliminates user-selected initial contours by incorporating the convex energy function. Additionally, the fractional differentiation reduces noises while preserving more detail information. Experiments on synthetic and real noisy images demonstrate that our proposed model surpasses other denoising VLSMs in terms of noise reduction, segmentation accuracy, and efficiency.

Fractional guidance-based level set evolution for noisy image segmentation with intensity inhomogeneity

Intensity inhomogeneity (IIH) and noise are ubiquitous in images acquired by a variety of imaging modalities, which pose an ongoing challenge for segmentation methods used in many applications. This paper proposes a level set evolution equation based on fractional derivative to address the IIH and noise issues in image segmentation, which integrates the information from the input and guidance images. The proposed model is a linear diffusion equation of level set function with nonlinear edge-stopping and guidance sources, in which the edge-stopping source is derived from the input image and a local statistical model (Bayesian formulation) in the level set framework, whereas the guidance source is derived from the guidance image and the fitting term of the popular Chan-Vese model. The guidance source ensures swift contour movement toward the boundary of the objects and enhances the robustness to noise and contour initialization, while the edge-stopping source is used to stop the evolving contour on the boundary of the objects later in the evolution. To tackle the challenge of obtaining accurate guidance images for many applications, an adaptive variable-order of fractional derivative robust to IIH and noise is introduced to generate the desired guidance image from the input image. The proposed diffusion equation with sources is solved numerically by the series splitting method and finite difference scheme. Experimental results on synthetic and real images show that the proposed model has higher performance in terms of accuracy and efficiency of segmentation, compared to several state-of-the-art level set models.

An optimized denoised bias correction model with local pre-fitting function for weak boundary image segmentation

The active contour model (ACM) plays a paramount part in grasping visual properties of images and exacting targets of interest. It is overwhelming hardship for traditional ACMs to segment images with noise, intensity inhomogeneity or low contrast and consider computation speed for practical applicability. Therefore, an optimized denoised bias correction (ODBC) model incorporating the pre-piecewise fitting function and the variational denoised term into the energy function is proposed for images with low contrast and intensity inhomogeneity. An optimized gradient descent equation and an upgraded regularization term are produced to enhance the robustness and sensitivity of this model. Experiments are conducted to validate that ODBC model possesses the superiority of reliability and speed in segmenting images with inhomogeneous intensity and strengthens the robustness to initial contours. The results manifest that mIOU of ODBC model exceeds 0.9, superior to prime ACMs and deep learning schemes, in segmenting images associated and unrelated with the dataset.

An interactive nuclei segmentation framework with Voronoi diagrams and weighted convex difference for cervical cancer pathology images

Objective. Nuclei segmentation is crucial for pathologists to accurately classify and grade cancer. However, this process faces significant challenges, such as the complex background structures in pathological images, the high-density distribution of nuclei, and cell adhesion. Approach. In this paper, we present an interactive nuclei segmentation framework that increases the precision of nuclei segmentation. Our framework incorporates expert monitoring to gather as much prior information as possible and accurately segment complex nucleus images through limited pathologist interaction, where only a small portion of the nucleus locations in each image are labeled. The initial contour is determined by the Voronoi diagram generated from the labeled points, which is then input into an optimized weighted convex difference model to regularize partition boundaries in an image. Specifically, we provide theoretical proof of the mathematical model, stating that the objective function monotonically decreases. Furthermore, we explore a postprocessing stage that incorporates histograms, which are simple and easy to handle and prevent arbitrariness and subjectivity in individual choices. Main results. To evaluate our approach, we conduct experiments on both a cervical cancer dataset and a nasopharyngeal cancer dataset. The experimental results demonstrate that our approach achieves competitive performance compared to other methods. Significance. The Voronoi diagram in the paper serves as prior information for the active contour, providing positional information for individual cells. Moreover, the active contour model achieves precise segmentation results while offering mathematical interpretability.

Few-shot segmentation framework for lung nodules via an optimized active contour model.

Accurate segmentation of lung nodules is crucial for the early diagnosis and treatment of lung cancer in clinical practice. However, the similarity between lung nodules and surrounding tissues has made their segmentation a longstanding challenge. Existing deep learning and active contour models each have their limitations. This paper aims to integrate the strengths of both approaches while mitigating their respective shortcomings. In this paper, we propose a few-shot segmentation framework that combines a deep neural network with an active contour model. We introduce heat kernel convolutions and high-order total variation into the active contour model and solve the challenging nonsmooth optimization problem using the alternating direction method of multipliers. Additionally, we use the presegmentation results obtained from training a deep neural network on a small sample set as the initial contours for our optimized active contour model, addressing the difficulty of manually setting the initial contours. We compared our proposed method with state-of-the-art methods for segmentation effectiveness using clinical computed tomography (CT) images acquired from two different hospitals and the publicly available LIDC dataset. The results demonstrate that our proposed method achieved outstanding segmentation performance according to both visual and quantitative indicators. Our approach utilizes the output of few-shot network training as prior information, avoiding the need to select the initial contour in the active contour model. Additionally, it provides mathematical interpretability to the deep learning, reducing its dependency on the quantity of training samples.

Soliton resolution for the complex short-pulse positive flow with weighted Sobolev initial data in the space-time soliton regions

The Riemann Hilbert approach and ∂‾ steepest descent method are used to study the Cauchy problem of a new complex short-pulse positive flow, from which the long-time asymptotics and soliton resolution conjecture for the complex short-pulse positive flow with initial conditions is obtained in weighted Sobolev spaces. Resorting to the spectral analysis of Lax pair, the introduced transformations of field variables and transformations of independent variables, we establish the basic Riemann-Hilbert problem, and convert the solution of the Cauchy problem of the complex short-pulse positive flow into the corresponding solution of the Riemann Hilbert problem. The jump matrix then expands and deforms continuously over the initial contour. We finally obtain the long-time asymptotics and soliton resolution for the complex short-pulse positive flow in the soliton region by using the ∂‾ steepest descent method. The results also show that N-soliton solutions of the complex short-pulse positive flow are asymptotically stable.

LEACS: a learnable and efficient active contour model with space-frequency pooling for medical image segmentation.

Diseases can be diagnosed and monitored by extracting regions of interest (ROIs) from medical images. However, accurate and efficient delineation and segmentation of ROIs in medical images remain challenging due to unrefined boundaries, inhomogeneous intensity and limited image acquisition. To overcome these problems, we propose an end-to-end learnable and efficient active contour segmentation model, which integrates a global convex segmentation (GCS) module into a light-weighted encoder-decoder convolutional segmentation network with a multiscale attention module (ED-MSA). The GCS automatically obtains the initialization and corresponding parameters of the curve deformation according to the prediction map generated by the ED-MSA, while provides the refined object boundary prediction for ED-MSA optimization. To provide precise and reliable initial contour for the GCS, we design the space-frequency pooling operation layers in the encoder stage of ED-MSA, which can effectively reduce the number of iterations of the GCS. Beside, we construct ED-MSA using the depth-wise separable convolutional residual module to mitigate the overfitting of the model. The effectiveness of our method is validated on four challenging medical image datasets. Code is here:https://github.com/Yang-fashion/ED-MSA_GCS.

Extraction of Building Roof Contours from Airborne LiDAR Point Clouds Based on Multidirectional Bands

Because of the complex structure and different shapes of building contours, the uneven density distribution of airborne LiDAR point clouds, and occlusion, existing building contour extraction algorithms are subject to such problems as poor robustness, difficulty with setting parameters, and low extraction efficiency. To solve these problems, a building contour extraction algorithm based on multidirectional bands was proposed in this study. Firstly, the point clouds were divided into bands with the same width in one direction, the points within each band were vertically projected on the central axis in the band, the two projection points with the farthest distance were determined, and their corresponding original points were regarded as the roof contour points; given that the contour points obtained based on single-direction bands were sparse and discontinuous, different banding directions were selected to repeat the above contour point marking process, and the contour points extracted from the different banding directions were integrated as the initial contour points. Then, the initial contour points were sorted and connected according to the principle of joining the nearest points in the forward direction, and the edges with lengths greater than a given threshold were recognized as long edges, which remained to be further densified. Finally, each long edge was densified by selecting the noninitial contour point closest to the midpoint of the long edge, and the densification process was repeated for the updated long edge. In the end, a building roof contour line with complete details and topological relationships was obtained. In this study, three point cloud datasets of representative building roofs were chosen for experiments. The results show that the proposed algorithm can extract high-quality outer contours from point clouds with various boundary structures, accompanied by strong robustness for point clouds differing in density and density change. Moreover, the proposed algorithm is characterized by easily setting parameters and high efficiency for extracting outer contours. Specific to the experimental data selected for this study, the PoLiS values in the outer contour extraction results were always smaller than 0.2 m, and the RAE values were smaller than 7%. Hence, the proposed algorithm can provide high-precision outer contour information on buildings for applications such as 3D building model reconstruction.

Phytoplankton Image Segmentation and Annotation Method Based on Microscopic Fluorescence.

Microscopic phytoplankton segmentation is an important part of water quality assessment. The segmentation of microscopic phytoplankton still faces challenges for computer vision, such as being affected by background impurities and requiring a large number of manual annotation. In this paper, the characteristics of phytoplankton emitting fluorescence under excitation light were utilized to segment and annotate phytoplankton contours by fusing fluorescence images and bright field images. Morphological operations were used to process microscopic fluorescence images to obtain the initial contours of phytoplankton. Then, microscopic bright field images were processed by Active Contour to fine tune the contours. Seven algae species were selected as the experimental objects. Compared with manually labeling the contour in LabelMe, the recall, precision, FI score and IOU of the proposed segmentation method are 85.3%, 84.5%, 84.7%, and 74.6%, respectively. Mask-RCNN was used to verify the correctness of labels annotated by the proposed method. The average recall, precision, F1 score and IOU are 97.0%, 86.5%, 91.1%, and 84.2%, respectively, when the Mask-RCNN is trained with the proposed automatic labeling method. And the results corresponding to manual labeling are 95.3%, 86.1%, 90.3%, and 82.8% respectively. The experimental results show that the proposed method can segment the phytoplankton microscopic image accurately, and the automatically annotated contour data has the same effect as the manually annotated contour data in Mask-RCNN, which greatly reduces the manual annotation workload.

Adaptive active contour model based on local bias field estimation and saliency

Some active contour models proposed based on intensity inhomogeneity are sensitive to initialization and cannot achieve ideal segmentation results for real images. An adaptive active contour model based on local bias field estimation and saliency is proposed in this paper. First of all, this model proposes an adaptive multi-local search algorithm, which avoids the initialization sensitivity by adaptively setting of the initial contour; Secondly, the local bias field is estimated by fusing the saliency map and fuzzy c-means clustering; Finally, the new bias field and the corrected energy fitting constant are used to define the new energy functional. The desired target object is obtained by minimizing the energy functional. The experimental results show that the segmentation accuracy of the model proposed in this paper is higher than that of the models participating in the comparison. The proposed model can not only avoid the interference of initialization and redundant information, but also segment images with intensity inhomogeneity effectively.

Feasibility study of adaptive radiotherapy with Ethos for breast cancer.

The aim of this study was to assess the feasibility of online adaptive radiotherapy with Ethos for breast cancer. This retrospective study included 20 breast cancer patients previously treated with TrueBeam. All had undergone breast surgery for different indications (right/left, lumpectomy/mastectomy) and were evenly divided between these four cases, with five extended cone beam computed tomography (CBCT) scans per patient. The dataset was used in an Ethos emulator to test the full adaptive workflow. The contours generated by artificial intelligence (AI) for the influencers (left and right breasts and lungs, heart) and elastic or rigid propagation for the target volumes (internal mammary chain (IMC) and clavicular lymph nodes (CLNs)) were compared to the initial contours delineated by the physician using two metrics: Dice similarity coefficient (DICE) and Hausdorff 95% distance (HD95). The repeatability of influencer generation was investigated. The times taken by the emulator to generate contours, optimize plans, and calculate doses were recorded. The quality of the scheduled and adapted plans generated by Ethos was assessed using planning target volume (PTV) coverage, homogeneity indices (HIs), and doses to organs at risk (OARs) via dose-volume histogram (DVH) metrics. Quality assurance (QA) of the treatment plans was performed using an independent portal dosimetry tool (EpiQA) and gamma index. On average, the DICE for the influencers was greater than 0.9. Contours resulting from rigid propagation had a higher DICE and a lower HD95 than those resulting from elastic deformation but remained below the values obtained for the influencers: DICE values were 0.79 ± 0.11 and 0.46 ± 0.17 for the CLN and IMC, respectively. Regarding the repeatability of the influencer segmentation, the DICE was close to 1, and the mean HD95 was strictly less than 0.15mm. The mean time was 73 ± 4 s for contour generation per AI and 80 ± 9 s for propagations. The average time was 53 ± 3 s for dose calculation and 125 ± 9 s for plan optimization. A dosimetric comparison of scheduled and adapted plans showed a significant difference in PTV coverage: dose received by 95% of the volume (D95%) values were higher and closer to the prescribed doses for adapted plans. Doses to organs at risk were similar. The average gamma index for quality assurance of adapted plans was 99.93 ± 0.38 for a 3%/3mm criterion. This study comprehensively evaluated the Ethos® adaptive workflow for breast cancer and its potential technical limitations. Although the results demonstrated the high accuracy of AI segmentation and the superiority of adapted plans in terms of target volume coverage, a medical assessment is still required.

Individual tooth segmentation in human teeth images using pseudo edge-region obtained by deep neural networks

In human teeth images taken outside the oral cavity with a general optical camera, it is difficult to segment individual tooth due to common obstacles such as weak edges, intensity inhomogeneities and strong light reflections. In this work, we propose a method for segmenting individual tooth in human teeth images. The key to this method is to obtain pseudo edge-region using deep neural networks. After an additional step to obtain initial contours for each tooth region, the individual tooth is segmented by applying active contour models. We also present a strategy using existing model-based methods for labeling the data required for neural network training.

Intelligent Interactive Deformable Image Registration for Online Adaptive Radiotherapy.

The goal of this study is to streamline the time-consuming contouring process in online adaptive radiotherapy (ART) by utilizing a deep learning-based interactive deformable image registration (DIR) algorithm. The objective is to minimize manual review and editing of automatically generated initial contours of organs-at-risk (OARs) and targets, thereby improving the efficiency and effectiveness of the treatment process. Our proposed method reforms the current DIR-based contour propagation method in clinical practice through the implementation of a deep learning-based interactive approach. The steps include: 1) generation of an initial deformable vector field (DVF) using a DL model, based on fixed and moving image pairs, resulting in the initial contours of OARs and targets; 2) clinician review/edit one the OAR/target contours as needed; 3) updated contour is sent to DL model to update the DVF and the remaining OARs/targets contours. Repeat this process until satisfactory contour qualities are achieved. We used the Open Access Series of Imaging Studies (OASIS) as the testbed, including 394 (train) and 20 (test) brain T1-weighted MRI scans, each containing 35 annotated organs. The U-Net architecture was employed to update the DVF from fixed/moving images, initial contours, and updated contours. We compared our approach to traditional manual editing without interaction and quantified the effort reduction using the added path length (APL) metric which is supposed to be proportional to the absolute time spent on the contour editing. We conducted paired t-test to show the significance. For comparison purpose, we assumed the clinicians edit the contours with the largest APL, i.e., the contours that require the most editing efforts. The editing effort, as measured by APL, was reduced by 18.5% to 25.4% with a mean of 23.3%, median of 23.6%, and standard deviation of 1.9%. The significance of the results was confirmed with a p-value of 1.47e-24. Our study demonstrates a significant reduction in editing effort, as measured by APL, compared to traditional manual contour editing. These results demonstrate the potential of our deep learning-based interactive approach to improve the efficiency and accuracy of the contouring process in clinical practice.

Adaptive Auto-Segmentation for MRI-Guided Online Adaptive Radiotherapy of Cervical Carcinoma

Accurate and efficient delineation of organs and targets on session images is critical in MRI-guided online adaptive radiotherapy (MRgOART). This study proposes a registration-guided deep learning image segmentation framework to assist online delineation of cervical carcinoma. A total of 300 T2-weighted MR images were acquired for patients with cervical carcinoma treated by a 1.5T Unity MR-Linac. The CTV, bladder, rectum, pelvic bone and femoral joints were delineated on each MRI by the same radiation oncologist. To overcome these obstacles to online MRI segmentation, we propose a registration-guided DL (RgDL) segmentation framework that integrates image registration algorithms and DL segmentation models. Firstly, the DL segmentation model was trained using nnU-net. Then, for each treatment fraction, the deformable image registration (DIR) algorithm generates initial contours from previous treatment fraction, which were used as guidance by DL model to obtain the accurate current segmentation. The segmentation accuracy of alone DIR, DL and RgDL were evaluated by dice similarity coefficients (DSC) and other distance-based metrics. Compared to the baseline approaches using the DIR and the DL alone, RgDL achieved a DSC of 91.12% on CTV, higher than DIR and DL alone by 15.54% and 10.13%. The DSC of RgDL were improved to 95.58%, 93.65%, 87.8% and 94.84% for bladder, pelvic bone, rectum and femoral joints, higher than DIR and DL alone by 9.61% on average. The proposed adaptive auto-segmentation method can achieve accurate and efficient segmentation and potentially overcome these obstacles to MRgOART.

A real-time contouring feedback tool for consensus-based contour training.

Variability in contouring structures of interest for radiotherapy continues to be challenging. Although training can reduce such variability, having radiation oncologists provide feedback can be impractical. We developed a contour training tool to provide real-time feedback to trainees, thereby reducing variability in contouring. We developed a novel metric termed localized signed square distance (LSSD) to provide feedback to the trainee on how their contour compares with a reference contour, which is generated real-time by combining trainee contour and multiple expert radiation oncologist contours. Nine trainees performed contour training by using six randomly assigned training cases that included one test case of the heart and left ventricle (LV). The test case was repeated 30 days later to assess retention. The distribution of LSSD maps of the initial contour for the training cases was combined and compared with the distribution of LSSD maps of the final contours for all training cases. The difference in standard deviations from the initial to final LSSD maps, ΔLSSD, was computed both on a per-case basis and for the entire group. For every training case, statistically significant ΔLSSD were observed for both the heart and LV. When all initial and final LSSD maps were aggregated for the training cases, before training, the mean LSSD ([range], standard deviation) was -0.8 mm ([-37.9, 34.9], 4.2) and 0.3mm ([-25.1, 32.7], 4.8) for heart and LV, respectively. These were reduced to -0.1 mm ([-16.2, 7.3], 0.8) and 0.1mm ([-6.6, 8.3], 0.7) for the final LSSD maps during the contour training sessions. For the retention case, the initial and final LSSD maps of the retention case were aggregated and were -1.5 mm ([-22.9, 19.9], 3.4) and -0.2 mm ([-4.5, 1.5], 0.7) for the heart and 1.8mm ([-16.7, 34.5], 5.1) and 0.2mm ([-3.9, 1.6],0.7) for the LV. A tool that uses real-time contouring feedback was developed and successfully used for contour training of nine trainees. In all cases, the utility was able to guide the trainee and ultimately reduce the variability of the trainee's contouring.

In silico trial of simulation-free hippocampal-avoidance whole brain adaptive radiotherapy

Background and PurposeHippocampal-avoidance whole brain radiotherapy (HA-WBRT) can be a time-consuming process compared to conventional whole brain techniques, thus potentially limiting widespread utilization. Therefore, we evaluated the in silico clinical feasibility, via dose-volume metrics and timing, by leveraging a computed tomography (CT)-based commercial adaptive radiotherapy (ART) platform and workflow in order to create and deliver patient-specific, simulation-free HA-WBRT. Materials and methodsTen patients previously treated for central nervous system cancers with cone-beam computed tomography (CBCT) imaging were included in this study. The CBCT was the adaptive image-of-the-day to simulate first fraction on-board imaging. Initial contours defined on the MRI were rigidly matched to the CBCT. Online ART was used to create treatment plans at first fraction. Dose-volume metrics of these simulation-free plans were compared to standard-workflow HA-WBRT plans on each patient CT simulation dataset. Timing data for the adaptive planning sessions were recorded. ResultsFor all ten patients, simulation-free HA-WBRT plans were successfully created utilizing the online ART workflow and met all constraints. The median hippocampi D100% was 7.8 Gy (6.6–8.8 Gy) in the adaptive plan vs 8.1 Gy (7.7–8.4 Gy) in the standard workflow plan. All plans required adaptation at first fraction due to both a failing hippocampal constraint (6/10 adaptive fractions) and sub-optimal target coverage (6/10 adaptive fractions). Median time for the adaptive session was 45.2 min (34.0–53.8 min). ConclusionsSimulation-free HA-WBRT, with commercially available systems, was clinically feasible via plan-quality metrics and timing, in silico.

Sequential Active Contour Based on Morphological-Driven Thresholding for Ultrasound Image Segmentation of Ascites.

Paracentesis is a high-demanding and routine operation, which has great potentials and benefits if semi-autonomous procedures can be developed. One of the most important techniques that facilitate semi-autonomous paracentesis is to segment the ascites from ultrasound images accurately and efficiently. The ascites, however, is usually with significantly different shapes and noise among different patients, and its shape/size changes dynamically during the paracentesis. This makes most of existing image segmentation methods either time consuming or inaccurate for segmenting ascites from its background. In this article, we propose a two-stage active contour method to facilitate accurate and efficient segmentation of ascites. First, a morphological-driven thresholding method is developed to locate the initial contour of the ascites automatically. Then, the identified initial contour is fed into a novel sequential active contour algorithm to segment the ascites from background accurately. The proposed method is tested and compared with state-of-the-art active contour methods on over 100 real ultrasound images of ascites, and the results show the superiority of our method in both accuracy and time efficiency.

A method framework of automatic localization and quantitative segmentation for the cavum septum pellucidum complex and the cerebellar vermis in fetal brain ultrasound images.

Early detection of central nervous system (CNS) anomalies in human embryos through prenatal screening is crucial for timely intervention and improved patient outcomes. Fetal brain mid-sagittal ultrasound images (FBMUIs) play a pivotal role as a diagnostic tool for detecting structural abnormalities. However, the automatic localization and quantitative segmentation of complex anatomical structures such as the corpus callosum-cavum septum pellucidum complex (CCC) and cerebellar vermis (CV) in FBMUIs present significant challenges. To address this issue, we propose an integrated framework that combines anatomical knowledge with computer vision techniques. Our framework comprises four steps: (I) generation of average templates for CCC and CV local images using a variational autoencoder (VAE); (II) localizing the CCC by using the "Initial Localization-Accurate Localization-Result Detection" strategy, followed by segmenting it based on morphological characteristics using the "Initial Contour Fitting-Contour Iteration" strategy; (III) applying a similar strategy as CCC localization and CV segmentation; and (IV) leveraging spatial and morphological characteristics to achieve accurate localization and segmentation. Our CCC and CV localization and segmentation methods were validated by using 140 FBMUIs from various perspectives. The accuracy and effectiveness of our approach were demonstrated through data statistics and comparative analysis. Currently, clinical trials are being conducted on our method at Shengjing Hospital of China Medical University. Our proposed integrated framework presents a novel solution for the automatic localization and quantitative segmentation of the CCC and CV in FBMUIs. It shows promise for early diagnosis of CNS anomalies in human embryos, offering significant clinical implications.