Objective: Breast cancer treatment often causes the removal of or damage to lymph nodes of the patient's lymphatic drainage system. This side effect is the origin of Breast Cancer-Related Lymphedema (BCRL), referring to a noticeable increase in excess arm volume. Ultrasound imaging is a preferred modality for the diagnosis and progression monitoring of BCRL because of its low cost, safety, and portability. As the affected and unaffected arms have similar appearances in B-mode ultrasound images, the thickness of the skin, subcutaneous fat, and muscle have been shown to be important biomarkers for this task. The segmentation masks are also helpful in monitoring the longitudinal changes in morphology and mechanical properties of each tissue layer. Methods: For the first time, a publicly available ultrasound dataset containing the Radio-Frequency (RF) data of 39 subjects as well as manual segmentation masks by two experts, are provided. Inter- and intra-observer reproducibility studies performed on the segmentation maps show a high Dice Score Coefficient (DSC) of $0.94\pm 0.08$ and $0.92\pm 0.06$ , respectively. Gated Shape Convolutional Neural Network (GSCNN) is modified for precise automatic segmentation of tissue layers, and its generalization performance is improved by the CutMix augmentation strategy. Results: We got an average DSC of $0.87\pm 0.11$ on the test set, which confirms the high performance of the method. Conclusion: Automatic segmentation methods can pave the way for convenient and accessible staging of BCRL, and our dataset can facilitate development and validation of those methods. Significance: Timely diagnosis and treatment of BCRL are of crucial importance to prevent irreversible damage.

Little is known about skeletal muscle thickness, echogenicity, and muscle quality (handgrip strength /muscle thickness) in breast cancer-related lymphedema (BCRL). Using B-mode ultrasound, we recorded the raw radiofrequency data of the affected (AA) and unaffected (UA) arms of women (n=20) with BCRL, and in 20 control women with no lymphedema (CA). The data were converted into images and measurements of skin, subcutaneous fat and muscle thickness were obtained. Within a designated region of interest, muscle echo-intensity was assessed using computer graded grey scale and muscle thickness was measured using ImageJ2. Handgrip strength was measured using standard dynamometry. We found that forearm muscle thickness was similar among AA, UA, and CA. When compared to the unaffected and control arms, the affected arm had a significantly higher (p 0.013) muscle echo-intensity. When compared to the AA, handgrip strength was similar to UA (p 0.401) but lower than CA (p 0.05). We conclude that the use of B-mode ultrasound is a reliable method to assess muscle thickness and EI in BCRL. When comparing AA to UA, the elevated levels of muscle EI in AA did not influence strength or muscle quality. The elevated EI in AA is a marker of abnormal health that needs to be further explored.