Towards automated spectroscopic tissue classification in thyroid and parathyroid surgery

A survey on recent trends in deep learning for nucleus segmentation from histopathology images

Nucleus segmentation is an imperative step in the qualitative study of imaging datasets, considered as an intricate task in histopathology image analysis. Segmenting a nucleus is an important part of diagnosing, staging, and grading cancer, but overlapping regions make it hard to separate and tell apart independent nuclei. Deep Learning is swiftly paving its way in the arena of nucleus segmentation, attracting quite a few researchers with its numerous published research articles indicating its efficacy in the field. This paper presents a systematic survey on nucleus segmentation using deep learning in the last five years (2017-2021), highlighting various segmentation models (U-Net, SCPP-Net, Sharp U-Net, and LiverNet) and exploring their similarities, strengths, datasets utilized, and unfolding research areas.

Near-Infrared Spectral Similarity between Ex Vivo Porcine and In Vivo Human Tissue

BACKGROUND

In vivo diffuse reflectance spectroscopy provides additional contrast in discriminating nerves embedded in adipose tissue during surgery. However, large datasets are required to achieve clinically acceptable classification levels. This study assesses the spectral similarity between ex vivo porcine and in vivo human spectral data of nerve and adipose tissue, as porcine tissue could contribute to generate large datasets.

METHODS

Porcine diffuse reflectance spectra were measured at 124 nerve and 151 adipose locations. A previously recorded dataset of 32 in vivo human nerve and 23 adipose tissue locations was used for comparison. In total, 36 features were extracted from the raw porcine to generate binary logistic regression models for all combinations of two, three, four and five features. Feature selection was performed by assessing similar means between normalized features of nerve and of adipose tissue (Kruskal-Wallis test, p < 0.05) and for models performing best on the porcine cross validation set. The human test set was used to assess classification performance.

RESULTS

The binary logistic regression models with selected features showed an accuracy of 60% on the test set.

CONCLUSIONS

Spectral similarity between ex vivo porcine and in vivo human adipose and nerve tissue was present, but further research is required.

Novel techniques for intraoperative parathyroid gland identification: a comprehensive review

INTRODUCTION

The parathyroid glands (PGs) are critical for calcium regulation and homeostasis. The preservation of PGs during neck surgery is crucial to avoid postoperative hypoparathyroidism. There are no existing guidelines for intraoperative PG identification, and the current approach relies heavily on the experience of the operating surgeon. A technique that accurately and rapidly identifies PGs would represent a useful intraoperative adjunct.

AREAS COVERED

This review aims to assess common dye and fluorescence-based PG imaging techniques and examine their utility for intraoperative PG identification. A literature search of published data on methylene blue (MB), indocyanine green (ICG) angiography, near-infrared autofluorescence (NIRAF), and the PGs between 1971 and 2020 was conducted on PubMed.

EXPERT OPINION

NIRAF and near-infrared (NIR) parathyroid angiography have emerged as promising and reliable techniques for intraoperative PG identification. NIRAF may aid with real-time identification of both normal and diseased PGs and reduce the risk of postoperative complications such as hypocalcemia. Further large prospective multicenter studies should be conducted in thyroid and parathyroid surgical patient populations to confirm the clinical efficacy of these intraoperative NIR-based PG detection techniques.

Classification of hyperspectral endocrine tissue images using support vector machines

BACKGROUND

Thyroidectomy is one of the most commonly performed surgical procedures. The region of the neck has a very complex structural organization. It would be beneficial to introduce a tool that can assist the surgeon in tissue discrimination during the procedure. One such solution is the noninvasive and contactless technique, called hyperspectral imaging (HSI).

METHODS

To interpret the HSI data, we implemented a supervised classification method to automatically discriminate the parathyroid, the thyroid, and the recurrent laryngeal nerve from surrounding tissue(muscle, skin) and materials (instruments, gauze). A leave-one-patient-out cross-validation was performed.

RESULTS

The best performance was obtained using support vector machine (SVM) with a classification and visualization in less than 1.4 seconds. A mean patient accuracy of 68% ± 23% was obtained for all tissues and material types.

CONCLUSIONS

The proposed method showed promising results and have to be confirmed on a larger cohort of patient data.

Robotic lateral cervical lymph node dissection via bilateral axillo-breast approach for papillary thyroid carcinoma: a single-center experience of 260 cases

To evaluate the feasibility and safety of robotic lateral cervical lymph node dissection via BABA, 260 thyroid cancer patients with suspected level II, III, IV, and Vb lymph node metastasis were selected. The lateral cervical compartment was exposed by splitting the sternocleidomastoid muscle longitudinally, and separating between the strap muscles and the anterior margin of the sternocleidomastoid muscle. The procedure was completed in 260 patients. Mean time for robotic lateral node dissection took 80 ± 21 min. The wound catheter was removed 6.3 days. Postoperative transient symptomatic hypocalcemia was observed in 51 patients, transient hoarseness in three, seroma in three, chyle leakage in two, and tracheal injury in one. 124 patients were confirmed to have lymph node metastasis on final pathological report. Average postoperative hospital stay was 6.5 days. Robotic lateral neck dissection by BABA is the acceptable operative alternative for thyroid cancer patients who wished to keep their surgical history private.