Two fully automated data-driven 3D whole-breast segmentation strategies in MRI for MR-based breast density using image registration and U-Net with a focus on reproducibility

Presence of higher breast density(BD)and persistence over time are risk factors for breast cancer.A quantitatively accurate and highly reproducible BD measure that relies on precise and reproducible whole-breast segmentation is desirable.In this study,we aimed to develop a highly reproducible and accurate whole-breast segmentation algorithm for the generation of reproducible BD measures.Three datasets of volunteers from two clinical trials were included.Breast MR images were acquired on 3T Siemens Biograph mMR,Prisma,and Skyra using 3D Cartesian six-echo GRE sequences with a fat-water separation technique.Two whole-breast segmentation strategies,utiliz-ing image registration and 3D U-Net,were developed.Manual segmentation was performed.A task-based analysis was performed:a previously developed MR-based BD measure,MagDensity,was calculated and assessed using automated and manual segmentation.The mean squared error(MSE)and intraclass correlation coefficient(ICC)between MagDensity were evaluated using the manual segmentation as a reference.The test-retest reproducibility of MagDensity derived from different breast segmentation methods was assessed using the difference between the test and retest measures(Δ_(2-1)),MSE,and ICC.The results showed that MagDensity derived by the registration and deep learning segmentation methods exhibited high concordance with manual segmentation,with ICCs of 0.986(95%CI:0.974-0.993)and 0.983(95%CI:0.961-0.992),respectively.For test-retest analysis,MagDensity derived using the regis-tration algorithm achieved the smallest MSE of 0.370 and highest ICC of 0.993(95%CI:0.982-0.997)when compared to other segmentation methods.In conclusion,the proposed registration and deep learning whole-breast segmentation methods are accurate and reliable for estimating BD.Both methods outperformed a previously developed algorithm and manual segmentation in the test-retest assessment,with the registration exhibiting superior performance for highly reproducible BD measurements.

Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

A prospective study comparing endoscopic subcutaneous mastectomy plus immediate reconstruction with implants and breast conserving surgery for breast cancer

Background Breast conserving surgery （BCS） has been the standard surgical procedure for the treatment of early breast cancer. Endoscopic subcutaneous mastectomy （ESM） plus immediate reconstruction with implants is an emerging procedure. The objective of this prospective study was to evaluate the clinical outcomes of these two surgical procedures in our clinical setting. Methods From March 2004 to October 2007, 43 patients with breast cancer underwent ESM plus axillary lymph node dissection and immediate reconstruction with implants, while 54 patients underwent BCS. The clinical and pathological characteristics, surgical safety, and therapeutic effects were compared between the two groups. Results There were no significant differences in the age, clinical stage, histopathologic type of tumor, operative blood loss, postoperative drainage time, and postoperative complications between the two groups （P 〉0.05）. The postoperative complications were partial necrosis of the nipple and superficial skin flap in the ESM patients, and hydrops in the axilla and residual cavity in the BCS patients. There was no significant difference in the rate of satisfactory postoperative cosmetic outcomes between the ESM （88.4%, 38/43） and BCS （92.6%, 50/54） patients （P 〉0.05）. During follow-up of 6 months to 4 years, all patients treated with ESM were disease-free, but 3 patients who underwent BCS had metastasis or recurrence -- one of these patients died of multiple organ metastasis. Conclusions After considering the wide indications for use, high surgical safety, and favorable cosmetic outcomes, we conclude that ESM plus axillary lymph node dissection and immediate reconstruction with implants -- the new surgery of choice for breast cancer -- warrants serious consideration as the prospective next standard surgical procedure.